In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
IRJET- Arrhythmia Detection using One Dimensional Convolutional Neural NetworkIRJET Journal
1) The document discusses using a 1D convolutional neural network to detect different types of arrhythmias from electrocardiogram (ECG) signals.
2) It proposes a novel wavelet domain multiresolution convolutional neural network approach that avoids complicated heartbeat detection techniques and heavy manual feature engineering.
3) The approach segments ECG signals, applies a discrete cosine transform to select coefficients, and uses a CNN for classification and arrhythmia monitoring. It detects five types of arrhythmias from one-lead ECG signals.
Personal identity verification based ECG biometric using non-fiducial features IJECEIAES
Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.
PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA C...IAEME Publication
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification.
This document presents a novel algorithm for automated detection of heartbeats in an electrocardiogram (ECG) signal using morphological filtering and Daubechies wavelet transform. The algorithm consists of three stages: 1) preprocessing using mathematical morphology operations to remove noise and baseline wander, 2) Daubechies wavelet transform decomposition to facilitate heartbeat detection, and 3) feature extraction to identify the QRS complex and detect heartbeats by analyzing the wavelet coefficients. Morphological filtering preserves the original ECG signal shape while removing impulsive noise, and wavelet transform aids in analyzing the non-stationary ECG signal. The algorithm aims to provide accurate and reliable heartbeat detection for diagnosing cardiac conditions.
IRJET- Investigation of Electroencephalogram Signals in Different Posture...IRJET Journal
This study investigated electroencephalogram (EEG) signals in radiologists observing clinical images in different postures during angiography procedures. Specifically, it examined attention levels and alpha and beta wave values in both standing and sitting positions. The study found no significant differences in attention levels or beta waves between postures, but did find significant differences in alpha wave values at several time points and in the ratio of beta to alpha waves. This suggests an image manipulation system using EEG signals could work in both standing and sitting postures by monitoring attention levels, which are not impacted by posture changes. The study demonstrated such a system developed previously could potentially be used in clinical situations.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Publishing House
This document discusses extracting respiratory rate from photoplethysmography (PPG) signals using principal component analysis (PCA) and empirical mode decomposition (EMD). It begins with an introduction to PPG signals and how they contain respiratory information. It then discusses previous efforts to extract respiratory signals from PPG that used methods like filtering and wavelets. The document proposes using PCA and EMD to improve upon existing methods. It provides background on PCA, EMD, and reviews literature on extracting respiratory information from ECG and how respiration modulates PPG signals. The aim is to evaluate different signal processing techniques to extract respiratory information from commonly available biomedical signals like ECG and PPG to avoid using additional sensors.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
IRJET- Arrhythmia Detection using One Dimensional Convolutional Neural NetworkIRJET Journal
1) The document discusses using a 1D convolutional neural network to detect different types of arrhythmias from electrocardiogram (ECG) signals.
2) It proposes a novel wavelet domain multiresolution convolutional neural network approach that avoids complicated heartbeat detection techniques and heavy manual feature engineering.
3) The approach segments ECG signals, applies a discrete cosine transform to select coefficients, and uses a CNN for classification and arrhythmia monitoring. It detects five types of arrhythmias from one-lead ECG signals.
Personal identity verification based ECG biometric using non-fiducial features IJECEIAES
Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.
PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA C...IAEME Publication
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification.
This document presents a novel algorithm for automated detection of heartbeats in an electrocardiogram (ECG) signal using morphological filtering and Daubechies wavelet transform. The algorithm consists of three stages: 1) preprocessing using mathematical morphology operations to remove noise and baseline wander, 2) Daubechies wavelet transform decomposition to facilitate heartbeat detection, and 3) feature extraction to identify the QRS complex and detect heartbeats by analyzing the wavelet coefficients. Morphological filtering preserves the original ECG signal shape while removing impulsive noise, and wavelet transform aids in analyzing the non-stationary ECG signal. The algorithm aims to provide accurate and reliable heartbeat detection for diagnosing cardiac conditions.
IRJET- Investigation of Electroencephalogram Signals in Different Posture...IRJET Journal
This study investigated electroencephalogram (EEG) signals in radiologists observing clinical images in different postures during angiography procedures. Specifically, it examined attention levels and alpha and beta wave values in both standing and sitting positions. The study found no significant differences in attention levels or beta waves between postures, but did find significant differences in alpha wave values at several time points and in the ratio of beta to alpha waves. This suggests an image manipulation system using EEG signals could work in both standing and sitting postures by monitoring attention levels, which are not impacted by posture changes. The study demonstrated such a system developed previously could potentially be used in clinical situations.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Publishing House
This document discusses extracting respiratory rate from photoplethysmography (PPG) signals using principal component analysis (PCA) and empirical mode decomposition (EMD). It begins with an introduction to PPG signals and how they contain respiratory information. It then discusses previous efforts to extract respiratory signals from PPG that used methods like filtering and wavelets. The document proposes using PCA and EMD to improve upon existing methods. It provides background on PCA, EMD, and reviews literature on extracting respiratory information from ECG and how respiration modulates PPG signals. The aim is to evaluate different signal processing techniques to extract respiratory information from commonly available biomedical signals like ECG and PPG to avoid using additional sensors.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Analysis electrocardiogram signal using ensemble empirical mode decomposition...IAEME Publication
This document discusses techniques for analyzing electrocardiogram (ECG) signals that are noisy and non-stationary. It compares the Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Discrete Wavelet Transform (DWT) for denoising ECG signals, finding that EEMD performs best by preserving true waveform features while eliminating noise. It also analyzes normal and abnormal (atrial fibrillation) ECG signals using parametric (periodogram, capon, time-varying autoregressive) and non-parametric (S-transform, smoothed pseudo affine Wigner distributions) time-frequency techniques, determining that the periodogram technique provides the best resolution
Classification of cardiac vascular disease from ecg signals for enhancing mod...hiij
“Why to be in frustration we will do new creation f
or salvation”. Based on these words we grapes your
attention towards saving a life of a heart patient
with the use of ECG in Public Health Care Center by
transmitting ECG signals to nearby hospital server.
In this paper we analyze the abnormalities found i
n the
ECG signals by identifying the Normal, Bradycardia
Arrhythmia, Tachycardia Arrhythmia and Ischemia
signal using the method of Neuro Fuzzy Classifier.
Daubechies Wavelet Transforms is used for feature
extraction and Adaptive Neuro Fuzzy Inference Syste
m (ANFIS) is used for classification. The compressi
on
algorithm is performed by using Huffman coding.
Photoplethysmography (PPG) and Phonocardiography (PCG) are two important non-invasive techniques for monitoring physiological parameters of cardiovascular diagnostics. The PCG signal discloses information about cardiac function through vibrations caused by the working heart. PPG measures relative blood volume changes in the blood vessels close to the skin. This paper emphasizes on simultaneous acquisition of PCG and PPG signals from the same subject with the aid of NIELVIS II+ DAQ and the signals are imported to MATLAB for further processing. Heart rate is extracted from both the signals which are found to be distinctive. This analytical approach of processing these signals can abet for analysis of Heart rate variability (HRV) which is widely used for quantifying neural cardiac control and low variability is particularly predictive of death in patients after myocardial infarction.
Electrocardiogram (ECG) flag is the electrical action of the human heart. The ECG contains imperative data about the general execution of the human heart framework. In this way, exact examination of the ECG flag is extremely critical however difficult undertaking. ECG flag is regularly low adequacy and polluted with various kinds of commotions due to its estimation procedure e.g. control line obstruction, amplifier clamor and standard meander. Benchmark meander is a sort of organic commotion caused by the arbitrary development of patient amid ECG estimation and misshapes the ST fragment of the ECG waveform. In this paper, we present a far reaching near investigation of five generally utilized versatile filtering calculations for the evacuation of low recurrence clamor. We perform broad investigations on the Physionet MIT BIH ECG database and contrast the flag with commotion proportion (SNR), combination rate, and time many-sided quality of these calculations. It is discovered that modified LMS has better execution than others regarding SNR and assembly rate.
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...IRJET Journal
This document presents a design for analyzing electrocardiogram (ECG) signals using an FPGA. It employs a least-square linear phase finite impulse response filter to remove noise from the ECG signal. It then uses discrete wavelet transform for feature extraction and a backpropagation neural network classifier to classify the ECG signal as normal or abnormal. If abnormal, a support vector machine is used to detect the type of heart disease. The system is implemented on a Xilinx FPGA using MATLAB.
IRJET - ECG based Cardiac Arrhythmia Detection using a Deep Neural NetworkIRJET Journal
This paper presents a system for detecting cardiac arrhythmias based on electrocardiogram (ECG) signals using a deep neural network. ECG signals are first transformed into time-frequency spectrograms using short-time Fourier transform. These spectrograms are then used as input for a 2D convolutional neural network to classify five types of arrhythmias: normal beat, normal sinus rhythm, atrial fibrillation, supraventricular tachycardia, and atrial premature beat. The technique is evaluated on the MIT-BIH database and achieves 97% beat classification accuracy and perfect rhythm identification. Compared to other existing methods like SVM, RNN, RF and KNN, the deep learning approach provides better performance for E
Real Time Signal Quality Aware Internet of Things IOT Framework for FPGA Base...ijtsrd
Day by day the scope and use of the electronics concepts in bio-medical field is increasing gradually. A novel approach to the design of real time ECG signal acquisition system for patient monitoring in medical application, FPGA Field Programmable Gate Array is the core heart of proposed system which is configured and programmed to acquire using ECG Electrocardiogram sensor. In this paper a new concept of ECG telemetry system is discussed along with signal quality aware IoT framework for energy efficient ECG monitoring system. Tele monitoring is a medical practice that involves monitoring patients who are not at the same location as the healthcare provider. The purpose of the present study is use to identify heart condition and give the information to the doctor. The objective of the study is to improve the doctor-patient ratio and evaluation of cardiac diseases in the rural population. The proposed system for the electrocardiogram ECG monitoring controlled by FPGA and implemented in the form of android application. Dhanashri P. Yamagekar | Dr. P. C. Bhaskar "Real Time Signal Quality Aware Internet of Things (IOT) Framework for FPGA Based ECG Telemetry System and Development of Android Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18938.pdf
http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18938/real-time-signal-quality-aware-internet-of-things-iot-framework-for-fpga-based-ecg-telemetry-system-and-development-of-android-application/dhanashri-p-yamagekar
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Biomedical Signal Processing / Biomedical Signals/ Bio-signals/ Bio-signals C...Mehak Azeem
These amazing and highly informative slides presented to the IEEE Signal Processing Society of IEEE MESCE Student Branch. These slides aim to provide basic knowledge about biosignals, their classification, examples and their working.
For more information, please contact:
[mehakazeem@ieee.org]
Design and development of electro optical system for acquisition of ppg signa...eSAT Publishing House
This document describes the design and development of an electro-optical photoplethysmography (PPG) system for acquiring PPG signals to assess the cardiovascular system. The system uses a light emitting diode and photodetector in a finger probe to non-invasively measure blood volume changes with each heartbeat. The acquired PPG signals are digitized and wirelessly transmitted using ZigBee technology. Preliminary testing on normal subjects found the pulse rates measured by the developed system to be accurate and in good agreement with a standard pulse oximeter system. The wireless PPG system allows for remote monitoring of cardiovascular parameters and has potential applications in ambulatory monitoring and postoperative care.
Evaluation of patient electrocardiogram datasets using signal quality indexingjournalBEEI
Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.
An Automated ECG Signal Diagnosing Methodology using Random Forest Classifica...ijtsrd
In this project, we put forward a new automated quality aware ECG beat classification method for effectual diagnosis of ECG arrhythmias under unsubstantiated health concern environments. The suggested method contains three foremost junctures i ECG signal quality assessment ECG SQA based whether it is “acceptable†or “unacceptable†based on our preceding adapted complete ensemble empirical mode decomposition CEEMD and temporal features, ii reconstruction of ECG signal and R peak detection iii the ECG beat classification as well as the ECG beat extraction, beat alignment and Random forest RF based beat classification. The accuracy and robustness of the anticipated method is evaluated by means of different normal and abnormal ECG signals taken from the standard MIT BIH arrhythmia database. The suggested ECG beat extraction approach can recover the categorization accuracy by protecting the QRS complex portion and background noises is suppressed under an acceptable level of noise . The quality aware ECG beat classification techniques attains higher kappa values for the classification accuracies which can be reliable as evaluated to the heartbeat classification methods without the ECG quality assessment process. Akshara Jayanthan M B | Prof. K. Kalai Selvi "An Automated ECG Signal Diagnosing Methodology using Random Forest Classification with Quality Aware Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30750.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30750/an-automated-ecg-signal-diagnosing-methodology-using-random-forest-classification-with-quality-aware-techniques/akshara-jayanthan-m-b
International Journal of Computational Engineering Research(IJCER) ijceronline
This paper proposes a lightweight, low-cost wearable ECG monitoring device using digital signal processing. An ECG acquisition system is designed using electrodes, instrumentation amplifiers, and filters to capture and preprocess the ECG signal. A PIC microcontroller detects the R-peak in the ECG to calculate heart rate. Results from testing on patients matched clinical analysis. The system aims to remotely monitor patients at low cost to detect cardiac issues earlier. Future work includes transmitting the digital ECG data wirelessly to doctors for remote monitoring and analysis.
This document describes the development of a low-cost and portable ECG monitoring system for rural/remote areas of Bangladesh. An ECG circuit was designed using inexpensive components like electrodes, voltage regulators, an instrumentation amplifier, low-pass filter, and clamper. The ECG signal was interfaced with an Arduino and sent to a PC/laptop for real-time monitoring and offline analysis using MATLAB. Digital signal processing techniques like filtering were used to remove noise from the ECG signal. An algorithm was then used to detect heartbeats and calculate diagnostic parameters from the ECG recording. The goal is to provide a complete low-cost solution for ECG monitoring and generation of patient reports that can help diagnose cardiac conditions in
Training feedforward neural network using genetic algorithm to diagnose left ...TELKOMNIKA JOURNAL
In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the ECG signal using windowed filtering technique. These features were applied to the conventional classifier and also to the neural network classifier with the modified weights using a genetic algorithm. The weights were modified by combining the crossover operators such as crossover arithmetic and crossover two-point operator. The results were compared with the various classifiers and the performance of the neural network with the modified weights using a genetic algorithm is outperformed. The accuracy of the weights modified feedforward neural network is 97.5%.
Artifact elimination in ECG signal using wavelet transformTELKOMNIKA JOURNAL
Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density.
IRJET-Estimation of Meditation Effect on Attention Level using EEGIRJET Journal
This document discusses a study that investigates the effect of meditation on attention level using EEG data analysis. EEG data was collected from subjects during meditation and non-meditation states. The data was preprocessed to remove noise and artifacts. Statistical features were then extracted from the EEG data, including standard deviation, relative power, average power spectral density, and entropy. A random forest classification method was used to analyze the data and detect attention states, achieving 90% accuracy. The study aims to objectively measure attention levels and the impact of meditation using EEG analysis to better understand cognitive disorders like ADHD.
Heart rate detection using hilbert transformeSAT Journals
Abstract The electrocardiogram (ECG) is a well known method that can be used to measure Heart Rate Variability (HRV). This paper describes a procedure for processing electrocardiogram signals (ECG) to detect Heart Rate Variability (HRV). In recent years, there have been wide-ranging studies on Heart rate variability in ECG signals and analysis of Respiratory Sinus Arrhythmia (RSA). Normally the Heart rate variability is studied based on cycle length variability, heart period variability, RR variability and RR interval tachogram. The HRV provides information about the sympathetic-parasympathetic autonomic stability and consequently about the risk of unpredicted cardiac death. The heart beats in ECG signal are detected by detecting R-Peaks in ECG signals and used to determine useful information about the various cardiac abnormalities. The temporal locations of the R-wave are identified as the locations of the QRS complexes. In the presence of poor signal-to-noise ratios or pathological signals and wrong placement of ECG electrodes, the QRS complex may be missed or falsely detected and may lead to poor results in calculating heart beat in turn inter-beat intervals. We have studied the effects of number of common elements of QRS detection methods using MIT/BIH arrhythmia database and devised a simple and effective method. In this method, first the ECG signal is preprocessed using band-pass filter; later the Hilbert Transform is applied on filtered ECG signal to enhance the presence of QRS complexes, to detect R-Peaks by setting a threshold and finally the RR-intervals are calculated to determine Heart Rate. We have implemented our method using MATLAB on ECG signal which is obtained from MIT/BIH arrhythmia database. Our MATLAB implementation results in the detection of QRS complexes in ECG signal, locate the R-Peaks, computes Heart Rate (HR) by calculating RR-internal and plotting of HR signal to show the information about HRV. Index Terms: ECG, QRS complex, R-Peaks, HRV, Heart Rate signal, RSA, Hilbert Transform, Arrhythmia, MIT/BIH, MATLAB and Lynn’s filters
IRJET- A Survey on Classification and identification of Arrhythmia using Mach...IRJET Journal
This document summarizes a survey on classifying and identifying arrhythmias using machine learning techniques. The survey examines existing research that uses techniques like support vector machines, neural networks, and deep learning algorithms to classify arrhythmias based on features extracted from electrocardiogram (ECG) signals. The proposed system aims to classify four types of arrhythmias (normal rhythm, left/right bundle branch block, premature ventricular contraction) from the MIT-BIH arrhythmia database with high accuracy by optimizing the combination of preprocessing, feature extraction, and classification methods. Performance will be evaluated based on sensitivity, specificity, and accuracy metrics.
Neural Network-Based Automatic Classification of ECG Signals with Wavelet Sta...IRJET Journal
This document presents a neural network-based method for automatically classifying ECG signals into normal and abnormal categories based on wavelet statistical features. The method involves pre-processing the ECG signals from the MIT-BIH Arrhythmia database to remove noise, extracting features using discrete wavelet transformation, and using these features to train a multilayer perceptron neural network classifier. Evaluation on the test data achieved up to 100% accuracy for normal cases and 90% accuracy for abnormality detection, demonstrating the effectiveness of the proposed method.
IRJET- Detection of Abnormal ECG Signal using DWT Feature Extraction and CNNIRJET Journal
This document discusses a study that uses discrete wavelet transform (DWT) to extract features from electrocardiogram (ECG) signals and then uses a convolutional neural network (CNN) to classify the signals as normal or abnormal. DWT is used to represent the ECG signals at different resolutions, which allows numerical features to be extracted. A CNN is then trained on the extracted features to predict whether signals indicate normal or abnormal heart conditions. The goal is to develop an efficient early detection system for cardiovascular disease by combining DWT feature extraction and CNN classification of ECG signals.
Less computational approach to detect QRS complexes in ECG rhythmsCSITiaesprime
Electrocardiogram (ECG) signals are normally affected by artifacts that require manual assessment or use of other reference signals. Currently, Cardiographs are used to achieve basic necessary heart rate monitoring in real conditions. This work aims to study and identify main ECG features, QRS complexes, as one of the steps of a comprehensive ECG signal analysis. The proposed algorithm suggested an automatic recognition of QRS complexes in ECG rhythm. This method is designed based on several filter structure composes low pass, difference and summation filters. The filtered signal is fed to an adaptive threshold function to detect QRS complexes. The algorithm was validated and results were checked with experimental data based on sensitivity test.
Analysis electrocardiogram signal using ensemble empirical mode decomposition...IAEME Publication
This document discusses techniques for analyzing electrocardiogram (ECG) signals that are noisy and non-stationary. It compares the Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Discrete Wavelet Transform (DWT) for denoising ECG signals, finding that EEMD performs best by preserving true waveform features while eliminating noise. It also analyzes normal and abnormal (atrial fibrillation) ECG signals using parametric (periodogram, capon, time-varying autoregressive) and non-parametric (S-transform, smoothed pseudo affine Wigner distributions) time-frequency techniques, determining that the periodogram technique provides the best resolution
Classification of cardiac vascular disease from ecg signals for enhancing mod...hiij
“Why to be in frustration we will do new creation f
or salvation”. Based on these words we grapes your
attention towards saving a life of a heart patient
with the use of ECG in Public Health Care Center by
transmitting ECG signals to nearby hospital server.
In this paper we analyze the abnormalities found i
n the
ECG signals by identifying the Normal, Bradycardia
Arrhythmia, Tachycardia Arrhythmia and Ischemia
signal using the method of Neuro Fuzzy Classifier.
Daubechies Wavelet Transforms is used for feature
extraction and Adaptive Neuro Fuzzy Inference Syste
m (ANFIS) is used for classification. The compressi
on
algorithm is performed by using Huffman coding.
Photoplethysmography (PPG) and Phonocardiography (PCG) are two important non-invasive techniques for monitoring physiological parameters of cardiovascular diagnostics. The PCG signal discloses information about cardiac function through vibrations caused by the working heart. PPG measures relative blood volume changes in the blood vessels close to the skin. This paper emphasizes on simultaneous acquisition of PCG and PPG signals from the same subject with the aid of NIELVIS II+ DAQ and the signals are imported to MATLAB for further processing. Heart rate is extracted from both the signals which are found to be distinctive. This analytical approach of processing these signals can abet for analysis of Heart rate variability (HRV) which is widely used for quantifying neural cardiac control and low variability is particularly predictive of death in patients after myocardial infarction.
Electrocardiogram (ECG) flag is the electrical action of the human heart. The ECG contains imperative data about the general execution of the human heart framework. In this way, exact examination of the ECG flag is extremely critical however difficult undertaking. ECG flag is regularly low adequacy and polluted with various kinds of commotions due to its estimation procedure e.g. control line obstruction, amplifier clamor and standard meander. Benchmark meander is a sort of organic commotion caused by the arbitrary development of patient amid ECG estimation and misshapes the ST fragment of the ECG waveform. In this paper, we present a far reaching near investigation of five generally utilized versatile filtering calculations for the evacuation of low recurrence clamor. We perform broad investigations on the Physionet MIT BIH ECG database and contrast the flag with commotion proportion (SNR), combination rate, and time many-sided quality of these calculations. It is discovered that modified LMS has better execution than others regarding SNR and assembly rate.
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...IRJET Journal
This document presents a design for analyzing electrocardiogram (ECG) signals using an FPGA. It employs a least-square linear phase finite impulse response filter to remove noise from the ECG signal. It then uses discrete wavelet transform for feature extraction and a backpropagation neural network classifier to classify the ECG signal as normal or abnormal. If abnormal, a support vector machine is used to detect the type of heart disease. The system is implemented on a Xilinx FPGA using MATLAB.
IRJET - ECG based Cardiac Arrhythmia Detection using a Deep Neural NetworkIRJET Journal
This paper presents a system for detecting cardiac arrhythmias based on electrocardiogram (ECG) signals using a deep neural network. ECG signals are first transformed into time-frequency spectrograms using short-time Fourier transform. These spectrograms are then used as input for a 2D convolutional neural network to classify five types of arrhythmias: normal beat, normal sinus rhythm, atrial fibrillation, supraventricular tachycardia, and atrial premature beat. The technique is evaluated on the MIT-BIH database and achieves 97% beat classification accuracy and perfect rhythm identification. Compared to other existing methods like SVM, RNN, RF and KNN, the deep learning approach provides better performance for E
Real Time Signal Quality Aware Internet of Things IOT Framework for FPGA Base...ijtsrd
Day by day the scope and use of the electronics concepts in bio-medical field is increasing gradually. A novel approach to the design of real time ECG signal acquisition system for patient monitoring in medical application, FPGA Field Programmable Gate Array is the core heart of proposed system which is configured and programmed to acquire using ECG Electrocardiogram sensor. In this paper a new concept of ECG telemetry system is discussed along with signal quality aware IoT framework for energy efficient ECG monitoring system. Tele monitoring is a medical practice that involves monitoring patients who are not at the same location as the healthcare provider. The purpose of the present study is use to identify heart condition and give the information to the doctor. The objective of the study is to improve the doctor-patient ratio and evaluation of cardiac diseases in the rural population. The proposed system for the electrocardiogram ECG monitoring controlled by FPGA and implemented in the form of android application. Dhanashri P. Yamagekar | Dr. P. C. Bhaskar "Real Time Signal Quality Aware Internet of Things (IOT) Framework for FPGA Based ECG Telemetry System and Development of Android Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18938.pdf
http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18938/real-time-signal-quality-aware-internet-of-things-iot-framework-for-fpga-based-ecg-telemetry-system-and-development-of-android-application/dhanashri-p-yamagekar
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Biomedical Signal Processing / Biomedical Signals/ Bio-signals/ Bio-signals C...Mehak Azeem
These amazing and highly informative slides presented to the IEEE Signal Processing Society of IEEE MESCE Student Branch. These slides aim to provide basic knowledge about biosignals, their classification, examples and their working.
For more information, please contact:
[mehakazeem@ieee.org]
Design and development of electro optical system for acquisition of ppg signa...eSAT Publishing House
This document describes the design and development of an electro-optical photoplethysmography (PPG) system for acquiring PPG signals to assess the cardiovascular system. The system uses a light emitting diode and photodetector in a finger probe to non-invasively measure blood volume changes with each heartbeat. The acquired PPG signals are digitized and wirelessly transmitted using ZigBee technology. Preliminary testing on normal subjects found the pulse rates measured by the developed system to be accurate and in good agreement with a standard pulse oximeter system. The wireless PPG system allows for remote monitoring of cardiovascular parameters and has potential applications in ambulatory monitoring and postoperative care.
Evaluation of patient electrocardiogram datasets using signal quality indexingjournalBEEI
Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.
An Automated ECG Signal Diagnosing Methodology using Random Forest Classifica...ijtsrd
In this project, we put forward a new automated quality aware ECG beat classification method for effectual diagnosis of ECG arrhythmias under unsubstantiated health concern environments. The suggested method contains three foremost junctures i ECG signal quality assessment ECG SQA based whether it is “acceptable†or “unacceptable†based on our preceding adapted complete ensemble empirical mode decomposition CEEMD and temporal features, ii reconstruction of ECG signal and R peak detection iii the ECG beat classification as well as the ECG beat extraction, beat alignment and Random forest RF based beat classification. The accuracy and robustness of the anticipated method is evaluated by means of different normal and abnormal ECG signals taken from the standard MIT BIH arrhythmia database. The suggested ECG beat extraction approach can recover the categorization accuracy by protecting the QRS complex portion and background noises is suppressed under an acceptable level of noise . The quality aware ECG beat classification techniques attains higher kappa values for the classification accuracies which can be reliable as evaluated to the heartbeat classification methods without the ECG quality assessment process. Akshara Jayanthan M B | Prof. K. Kalai Selvi "An Automated ECG Signal Diagnosing Methodology using Random Forest Classification with Quality Aware Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30750.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30750/an-automated-ecg-signal-diagnosing-methodology-using-random-forest-classification-with-quality-aware-techniques/akshara-jayanthan-m-b
International Journal of Computational Engineering Research(IJCER) ijceronline
This paper proposes a lightweight, low-cost wearable ECG monitoring device using digital signal processing. An ECG acquisition system is designed using electrodes, instrumentation amplifiers, and filters to capture and preprocess the ECG signal. A PIC microcontroller detects the R-peak in the ECG to calculate heart rate. Results from testing on patients matched clinical analysis. The system aims to remotely monitor patients at low cost to detect cardiac issues earlier. Future work includes transmitting the digital ECG data wirelessly to doctors for remote monitoring and analysis.
This document describes the development of a low-cost and portable ECG monitoring system for rural/remote areas of Bangladesh. An ECG circuit was designed using inexpensive components like electrodes, voltage regulators, an instrumentation amplifier, low-pass filter, and clamper. The ECG signal was interfaced with an Arduino and sent to a PC/laptop for real-time monitoring and offline analysis using MATLAB. Digital signal processing techniques like filtering were used to remove noise from the ECG signal. An algorithm was then used to detect heartbeats and calculate diagnostic parameters from the ECG recording. The goal is to provide a complete low-cost solution for ECG monitoring and generation of patient reports that can help diagnose cardiac conditions in
Training feedforward neural network using genetic algorithm to diagnose left ...TELKOMNIKA JOURNAL
In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the ECG signal using windowed filtering technique. These features were applied to the conventional classifier and also to the neural network classifier with the modified weights using a genetic algorithm. The weights were modified by combining the crossover operators such as crossover arithmetic and crossover two-point operator. The results were compared with the various classifiers and the performance of the neural network with the modified weights using a genetic algorithm is outperformed. The accuracy of the weights modified feedforward neural network is 97.5%.
Artifact elimination in ECG signal using wavelet transformTELKOMNIKA JOURNAL
Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density.
IRJET-Estimation of Meditation Effect on Attention Level using EEGIRJET Journal
This document discusses a study that investigates the effect of meditation on attention level using EEG data analysis. EEG data was collected from subjects during meditation and non-meditation states. The data was preprocessed to remove noise and artifacts. Statistical features were then extracted from the EEG data, including standard deviation, relative power, average power spectral density, and entropy. A random forest classification method was used to analyze the data and detect attention states, achieving 90% accuracy. The study aims to objectively measure attention levels and the impact of meditation using EEG analysis to better understand cognitive disorders like ADHD.
Heart rate detection using hilbert transformeSAT Journals
Abstract The electrocardiogram (ECG) is a well known method that can be used to measure Heart Rate Variability (HRV). This paper describes a procedure for processing electrocardiogram signals (ECG) to detect Heart Rate Variability (HRV). In recent years, there have been wide-ranging studies on Heart rate variability in ECG signals and analysis of Respiratory Sinus Arrhythmia (RSA). Normally the Heart rate variability is studied based on cycle length variability, heart period variability, RR variability and RR interval tachogram. The HRV provides information about the sympathetic-parasympathetic autonomic stability and consequently about the risk of unpredicted cardiac death. The heart beats in ECG signal are detected by detecting R-Peaks in ECG signals and used to determine useful information about the various cardiac abnormalities. The temporal locations of the R-wave are identified as the locations of the QRS complexes. In the presence of poor signal-to-noise ratios or pathological signals and wrong placement of ECG electrodes, the QRS complex may be missed or falsely detected and may lead to poor results in calculating heart beat in turn inter-beat intervals. We have studied the effects of number of common elements of QRS detection methods using MIT/BIH arrhythmia database and devised a simple and effective method. In this method, first the ECG signal is preprocessed using band-pass filter; later the Hilbert Transform is applied on filtered ECG signal to enhance the presence of QRS complexes, to detect R-Peaks by setting a threshold and finally the RR-intervals are calculated to determine Heart Rate. We have implemented our method using MATLAB on ECG signal which is obtained from MIT/BIH arrhythmia database. Our MATLAB implementation results in the detection of QRS complexes in ECG signal, locate the R-Peaks, computes Heart Rate (HR) by calculating RR-internal and plotting of HR signal to show the information about HRV. Index Terms: ECG, QRS complex, R-Peaks, HRV, Heart Rate signal, RSA, Hilbert Transform, Arrhythmia, MIT/BIH, MATLAB and Lynn’s filters
IRJET- A Survey on Classification and identification of Arrhythmia using Mach...IRJET Journal
This document summarizes a survey on classifying and identifying arrhythmias using machine learning techniques. The survey examines existing research that uses techniques like support vector machines, neural networks, and deep learning algorithms to classify arrhythmias based on features extracted from electrocardiogram (ECG) signals. The proposed system aims to classify four types of arrhythmias (normal rhythm, left/right bundle branch block, premature ventricular contraction) from the MIT-BIH arrhythmia database with high accuracy by optimizing the combination of preprocessing, feature extraction, and classification methods. Performance will be evaluated based on sensitivity, specificity, and accuracy metrics.
Neural Network-Based Automatic Classification of ECG Signals with Wavelet Sta...IRJET Journal
This document presents a neural network-based method for automatically classifying ECG signals into normal and abnormal categories based on wavelet statistical features. The method involves pre-processing the ECG signals from the MIT-BIH Arrhythmia database to remove noise, extracting features using discrete wavelet transformation, and using these features to train a multilayer perceptron neural network classifier. Evaluation on the test data achieved up to 100% accuracy for normal cases and 90% accuracy for abnormality detection, demonstrating the effectiveness of the proposed method.
IRJET- Detection of Abnormal ECG Signal using DWT Feature Extraction and CNNIRJET Journal
This document discusses a study that uses discrete wavelet transform (DWT) to extract features from electrocardiogram (ECG) signals and then uses a convolutional neural network (CNN) to classify the signals as normal or abnormal. DWT is used to represent the ECG signals at different resolutions, which allows numerical features to be extracted. A CNN is then trained on the extracted features to predict whether signals indicate normal or abnormal heart conditions. The goal is to develop an efficient early detection system for cardiovascular disease by combining DWT feature extraction and CNN classification of ECG signals.
Less computational approach to detect QRS complexes in ECG rhythmsCSITiaesprime
Electrocardiogram (ECG) signals are normally affected by artifacts that require manual assessment or use of other reference signals. Currently, Cardiographs are used to achieve basic necessary heart rate monitoring in real conditions. This work aims to study and identify main ECG features, QRS complexes, as one of the steps of a comprehensive ECG signal analysis. The proposed algorithm suggested an automatic recognition of QRS complexes in ECG rhythm. This method is designed based on several filter structure composes low pass, difference and summation filters. The filtered signal is fed to an adaptive threshold function to detect QRS complexes. The algorithm was validated and results were checked with experimental data based on sensitivity test.
Feature extraction of electrocardiogram signal using machine learning classif...IJECEIAES
In the various field of life person identification is an essential and important task. This helps for the investigation of criminal activities and used in various type of forensic applications like surveillance. For biometric recognition iris, face, voice and fingerprint have a limited fabrication and from there the exact decision regarding liveliness of the subject can be drawn. The aim of the approach is to construct a biometric recognition system based on ECG which processes the raw ECG signal. The entire process is supported by different filters for noise elimination and ECG characteristics waves gone through time domain analysis. Based on the analysis an efficient feature extraction model is developed where several best P-QRS-T signal parts are taken and the positions of the fragmented signals are normalized depends on the priorities of their positions. The calculation of domain features done 72 times. It checks the data sets (train and test) and from feature vector matching to each of the individual signal, separately. The performance and utility of the system are analyzed and feature vectors are examined by different classification algorithms of machine learning. The leading algorithms like K-nearest neighbor, artificial neural network and support vector machine are used to classify different features of ECG, and it is tested using standard cardiac database i.e. the MIT-BIH ECG -ID database.
Electrocardiograph signal recognition using wavelet transform based on optim...IJECEIAES
Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased.
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
An electrocardiogram (ECG) is a bio-electrical signal which is used to record the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG).
This document discusses biometric recognition based on electrocardiogram (ECG) signals. It outlines the objective to comprehensively study biometrics systems using ECG signals. The document describes the typical components of such systems, including preprocessing raw ECG data, extracting features through symbolic representation, and classifying signals with algorithms like k-nearest neighbors. It also summarizes the results of experimental evaluations testing various feature extraction techniques on two ECG datasets, finding mean identification accuracies above 98%. The document concludes the potential value of ECG-based biometrics for personal identification.
Detection of EEG Spikes Using Machine Learning ClassifierIRJET Journal
This document discusses a study on detecting epileptic seizures from EEG data using machine learning classifiers. It begins with an introduction to epilepsy and EEG signals. Feature extraction is identified as an important step, as is understanding the statistical properties of the data. Previous studies that used time domain, frequency domain, and time-frequency domain features are summarized. Commonly used machine learning classifiers like SVMs, ANNs, and random forests are also mentioned. The methodology of the presented study involved recording EEG data from rats injected with penicillin to induce seizures, extracting time and frequency domain features, and using an SVM classifier to classify signals as epileptic or non-epileptic. The goal of the study was to analyze and identify features to classify EEG
Detection of Real Time QRS Complex Using Wavelet Transform IJECEIAES
This paper presents a novel method for QRS detection. To accomplish this task ECG signal was first filtered by using a third order Savitzky Golay filter. The filtered ECG signal was then preprocessed by a Wavelet based denoising in a real-time fashion to minimize the undefined noise level. R-peak was then detected from denoised signal after wavelet denoising. Windowing mechanism was also applied for finding any missing R-peaks. All the 48 records have been used to test the proposed method. During this testing, 99.97% sensitivity and 99.99% positive predictivity is obtained for QRS complex detection.
Wavelet-Based Approach for Automatic Seizure Detection Using EEG SignalsIRJET Journal
This document presents a wavelet-based approach for automatically detecting seizures using EEG signals. EEG data is decomposed into detailed and approximate coefficients using discrete wavelet transform up to the fourth level. Statistical features are extracted from the wavelet coefficients and the most significant features are selected using the Wilcoxon rank-sum test. Three classifiers - SVM, kNN, and ensemble subspace kNN - are used to classify EEG segments as pre-ictal, inter-ictal, or ictal. The proposed method achieves 100% classification accuracy when discriminating between healthy and epileptic EEG signals on the neurology and sleep centre EEG database.
APPLICATION OF 1D CNN IN ECG CLASSIFICATIONIRJET Journal
This document discusses applying a 1D convolutional neural network (CNN) to classify electrocardiogram (ECG) signals and detect cardiac abnormalities. Specifically:
1) A binary classifier using a 1D-CNN is implemented to detect suspect anomalies in ECGs, regardless of the type of cardiac pathology.
2) The model is tested on 21 categories of different cardiac pathologies classified as anomalies.
3) The goal is to have a system that can be implemented on low-cost portable devices to help detect potential cardiac issues from ECG readings.
Real time ecg signal analysis by using new data reduction algorithm forIAEME Publication
This document summarizes a research paper that proposes a new method for compressing electrocardiogram (ECG) signals for transmission over wireless personal area networks (WPANs). The method uses curvature analysis to select feature points in the ECG signal, including the P, Q, R, S, and T waves, which are important for diagnosis. Additional points are then selected iteratively to minimize reconstruction errors when decompressing the signal. The researchers conclude that the curvature-based method is able to preserve all important diagnostic features of the ECG signal while significantly compressing the data size for transmission over bandwidth-limited WPANs.
A Review on ECG -Signal Classification of Scalogram Snap shots the use of Con...IRJET Journal
This document summarizes previous research on classifying electrocardiogram (ECG) signals using convolutional neural networks (CNNs) and continuous wavelet transforms. It discusses how CNNs trained on large ECG datasets can classify cardiac arrhythmias with higher accuracy than experts. Previous studies used CNNs to extract features from ECG time-frequency spectrograms generated via short-time Fourier transforms or continuous wavelet transforms. The document reviews several studies that achieved high classification accuracy for different types of arrhythmias using these deep learning techniques. It also mentions the motivation is to help diagnosis and early detection of heart conditions, and the objective is to reduce classification time and increase accuracy.
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
Automatic ECG signal denoising and arrhythmia classification using deep learningIRJET Journal
This document presents a method for automatic ECG signal denoising and arrhythmia classification using deep learning. It proposes an automatic denoising method called LDAE that uses denoising autoencoders based on long short-term memory to remove noise from ECG signals. The denoised signals are then classified for different types of arrhythmias using a deep multilayer perceptron algorithm. The method achieves an average SNR of 27.5 and RMSE of 0.037 for signal reconstruction, demonstrating effective noise removal. For arrhythmia classification, the method obtains 98% accuracy, 98.57% precision, 97.25% recall, and 97.45% F1 score.
This document discusses a system for classifying and compressing cardiac vascular diseases using soft computing techniques to enhance rural healthcare. ECG signals are collected from patients and features are extracted from the signals using discrete wavelet transform. The features are classified using an adaptive neuro-fuzzy inference system to identify normal signals or one of four abnormal conditions. Signals classified as critical are compressed using Huffman coding and sent to a hospital server for treatment, while mild cases are advised to see a cardiologist. The system aims to help identify heart conditions early to save lives in rural areas with limited access to healthcare.
IRJET- Congestive Heart Failure Recognition by Analyzing The ECG Signals usi...IRJET Journal
This document summarizes a study that analyzed ECG signals to recognize congestive heart failure using wavelet coefficients. ECG data from 20 different disease cases were collected and filtered to remove noise. A wavelet transform was applied to obtain wavelet coefficients, which were used to estimate cardiac diseases from the 3D wavelet plot. The zero crossing algorithm was also used to calculate heart rate from the number of zero crossings in the ECG signal. Results found that wavelet coefficients and 3D plots provided useful information for analyzing ECG signals and recognizing different heart conditions and diseases.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Transmission of arm based real time ecg for monitoring remotely located patienteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A low-cost electro-cardiograph machine equipped with sensitivity and paper sp...TELKOMNIKA JOURNAL
This document summarizes a study that developed a low-cost electrocardiograph (ECG) machine with adjustable sensitivity and paper speed options. The ECG machine uses an ATmega16 microcontroller and includes a 12-channel ECG amplifier built with instrumentation amplifiers. Testing was conducted on 10 subjects and an ECG phantom to evaluate the machine's performance at different sensitivities (0.5 mV, 1 mV, 2 mV) and paper speeds (25 mm/s, 50 mm/s). Results showed the machine measured heart rates from the phantom within 2% error of the standard values and sensitivity measurements were also within specifications. The developed low-cost ECG machine achieved diagnostic-level performance with adjustable options, addressing
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
2. Int J Elec & Comp Eng ISSN: 2088-8708
Utilizing ECG Waveform Features as New Biometric Authentication Method (Ahmed Younes Shdefat)
659
signals features, which are classified into time, frequency and time–frequency extraction techniques. The
extracted time domain features are the amplitude parameters (QRS, ST), duration parameters (QRS, QT, and
PR) and heartbeat interval [2]. Time extraction technique do not provide good recognition; and that is due to
the frequent changes in the amplitude and duration in the ECG Signal waves [3]. On the other hand, the
frequency domain extraction technique do not provide interim information about the ECG signals waves.
Applying suitable time–frequency technique can overcome this issue.
In the current biometric authentication using ECG signal waves related works, many researches had
been performed; like in [1], the researchers have propused mobile biometric authentication algorithm based
on ECG signal. They have conducted two experiment types; experiment using physionet ECG records and
laboratory experiment. The first experiment type from their work is related directly to our work. So according
to them, 73 records obtained from the Physionet database. The ECG time length for the obtained records
scope between 30 mintutes to 24 hours. Their method had scord 1.29% false acceptance rate and 81.82%
accuracy rate over 4 s of signal lenght.
In the work [6], they had a stratigy to extort frequency doiman features using Fourier transform
method. They were seeking for certain features among the frequency doiman features; like slope, harmonic
number, the magnitude gap and ratio of different frequency energy to total energy. For the classification
process they had used correlation analysis and neural network were used in identification phase. They have
scored low recognition rate, they believed the reason was in feature selection method; but from our research
point of view we belive that the reason is in the feature extraction type due to insufficient information
provede by this type of feature. They have scored 96.4% of accuracy rate with 3.61 false accept rate.
We have contributed to biometric authentication filed by increasing the accuracy rate level for
authentication and classification phases; at the same time, we have reduced the false acceptance rate. We
have scored a significant authentication accuracy rate by following certain proper steps; starting from
preprocessing the raw ECG signal data, feature selection, segmentation and extraction technique. Also, we
have used the best classification method in ECG signal case uinsg linear SVM classifier and at the same time
we have used template matching technique for authentication process.
2. ECG SIGNAL PHYSIOLOGY
When the heart cells are in rest situation they considered as in polarized case which means that there
is no electrical activity is taking place at the moment. Basically, the heart is depolarized when it is in systole
state and repolarized when it is at diastole state. The depolarization case in normal ECG wave takes place
from the beginning the P wave until the middle of ST segment as shown in Figure 1, while the repolarization
case starts from the T wave [7], [8].
Figure 1. ECG signal physiology.
Broadly, ECG time–frequency features are classified as either waveform or characteristic based
features. The simplest one to obtain among the features is characteristic based, because in order to start the
extraction process ; it just require the ECG fiducial points information about one of ECG complex. In
Figure 1 PS, P, PE, Q, R, S, TS, T,TE are the fiducial points of the given portion from the ECG signal.
Fiducial points represent the correspondence points that intersect with the peaks and boundaries of P, QRS
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 2, April 2018 : 658 – 665
660
and T waves. In ideal ECG complex, there are six boundaries and three peaks (P, R, and T) as illustrated in
Figure 1 [9].
The main features that aimed to be extracted are angle, slope, amplitude and duration (temporal).
Discriminative characteristics must be selected from these features by applying extra processing over them
[10]. The problem of extracting characteristic based features is when we use fiducial points that may cause
difficulties in detecting the boundaries especially in the presence of noise.
On the other hand, Waveform based features processed by using the waveform process coefficient
values such as autocorrelation, fourier coefficient, phase space reconstruction, and wavelet coefficient. It is
worth mentioning that most of the ECG complexes are used in waveform technique for feature extraction.
The technique has an advantage of not requiring detection of the fiducial points which may generate
boundary detection difficulties in the presence of noise [11]. Therefore, inorder to avoid boundary detection
difficulties issue, we have extorted waveform based feature out of the ECG signal.
3. RESEARCH METHOD
Our method structure is organized by a sequence of processing steps; starting with ECG
preprocessing, ECG feature selection, Segmantaion, ECG feature extraction, Inter-Beat-Interval (IBI)
preprocessing and IBI extraction.
The first step in ECG preprocessing, is that we should filter the ECG to maximize beat detection
efficiency. High-pass filtering was used to remove the baseline wander, which is achieved by subtracting a
low frequency trend line from the ECG [12]. This trend line was formed by applying a triangular (two-pass)
moving average filter with a window size of 512 points and constent rate of 50 HZ to the ECG signal. On the
other hand, Low-pass filtering is used to remove high frequency noise by using a triangular moving average
filter with a window size of 3 points. Moving average filters provided a simple, valuable, and fast method to
filter ECG [13], [14].We have applied feature selection phase by using abs() Matlab built-in function to apply
the absolute value over the whole ECG signal data.
Right after the preprocessing comes the segmentation, ECG segments were processed for
30 seconds. This process starts by passing a moving window over the entire ECG data for 5 minutes [15].
Then, figures over ECG were collected for each window on time. According to the output, a list of functional
ECG segments was created. The selection criteria for the applicable 5 minute segment have been achieved by
the following:
a. Heart rate (HR) mean was more than 250 beats/min
b. The overall number of ectopic beats were less than 1% of total beats
c. The nonexistence of significant missing data, and the visual inspection of noise and ecotype.
d. Significant missing data was defined as having more than 150 consecutive missing data values or
missing more than 150ms consecutively.
e. Segments with less than 150 consecutive missing data values were interpolated using cubic splie.
Figure 2. Template matching for QRS detection
As a result, the applicable 5 minute segment with the lowest HR was chosen for analysis.
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For the ECG feature extraction, Correlation based template matching algorithm was used to locate
QRS complexes [16]. However, two QRS types of templates were implemented to achieve template matching
and they were created by averaging more than 20 QRS complexes [17]. The first type of template was the
Global template; global in the sense that one template was suitable for all subjects as shown inFigure 2.
In IBI preprocessing and before initiating the correction process over the ectopic intervals, we
should be acquainted with them. To achieve the proper detection techniques, we have used percentage,
standard deviation and median filters. The median filter acts as an impulse rejection filter with threshold to
delineate ectopic intervals [18,19]. A length of N with random variable x using a threshold of τ for median
filter equation is given by the following equation:
( )
| ( ) ( )|
*| ( ) ( )|+
(1)
If D (n) ≤ , then not ectopic; else ectopic
The correction process is established after detecting the ectopic interval by applying four correction
techniques. The correction process is established after detecting the ectopic interval by applying four
correction techniques [20]. The first one aims to eliminate any ectopic intervals could be found. The second
technique is used to replace any ectopic interval with the mean value of w near the IBI intervals centered on
the ectopic interval as shown in Equation (2). Median technique is related to the replacement of ectopic
intervals with the median value of w neighboring the IBI intervals centered on the ectopic interval as shown
in Equation (3). Finally, cubic spline replaces ectopic intervals by using cubic spline interpolation.
( ) * ( ) | | (2)
( ) * ( ) | | (3)
There are multiple mechanisms of signal detrending (normalizing) to remove low frequency, some
of these mechanisms are: linear detrending, polynomial detrending, wavelet detrending and wavelet packet
detrending.
In the IBI extraction step, the data contain beat-to-beat intervals that were basically elicited from the
ECG signals. Commonly, we rely on R-wave to find the temporal locations of beats as it is often the easiest
wave to distinguish. Moreover, R waves typically have the largest amplitudes in comparison with
surrounding P, Q, S, and T waveforms [21].
The following equation clarifies how to calculate time series IBI of an ECG segment containing N
beats, where beat (n) is the time location of the nth beat:
( ) ( ) ( ) (4)
The system model consists of a series of process start from recording a portion of 30 second from
the raw signal for the first time, Pre-process the raw signal, Extort the feature out of it then in the
authentication process the system will apply template matching technique as shown in Figure 3. After the
system will successfully recognized the user; the Linear SVM classifier will classify the users according to
authorization level associated with all granted privileges to each level such as “admin” or “regular user”
level. The system will display the user name, age, gender, ECG signal, spectrum, QRS complex, heart beat
based on signal and heart beat based on spectrum [22-24].
We have used LIBLINEAR [25], [26] over 8 subjects‟ ECG records for training set and 36 subjects‟
ECG records for testing set. We have applied linear SVM according to [27] recomandation; after they have
done a comparsion in their work between the linear and non-linear SVM performance. The subject can
provide the ECG signal to our system in many ways, one of the ways is by touching the mobile and bracelet
electrodes with the fingertips for first-time enrollment and template creation or by just toching the bracelet
that contains two electrodes that located in the upper side and under the bracelet like in Nymi band case. The
other way is by doing the ECG recording in clinic or hospital but it is not convineant one because it is not
practical due to some limitiations like it will not be avialabe all of the time to apply it.
We used the following equation to assess accuracy rate (AR) over the classification and
authentication phases of our experiment:
(5)
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Where variable represent the number of the samples that have tested correctly during the
classification and authentication phases. The variable represent the total number of 44 subjects' test
samples.
For the purpose of evaluating the authentication phase of our experiment, we have used the
following equations where Equation (6) is to obtain false accept rate (FAR):
FAR= (6)
In fact, in equation number 6; FPS represent the number of false positive samples and ANS
represent the number of actual negative samples.
Figure 3. ECG Authentication System Technical Approach
4. RESULTS AND ANALYSIS
We have tested 44 subjects‟ ECG raw records with the extensions (*.mat, *.txt, *.dat); 22 of the
subjects‟ samples were authorized inside our system as 11 males and 11 femeles as shown in Figure 3 while
the other 22 as 11 males and 11 femeles are not authorized users as shown in Figure 4. The accuracy rate was
97.2% for the authentication process and 98.6% for the classification task over all the segments of the whole
signal.
Figure 4. ECG Authentication System result
for authorized user
Figure 5. ECG Authentication System result
for unauthorized user
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In time, when we applied the SVM classifer over a portion of the signal which technicily one
segment out of the signal, we found that the classification accuracy average was 95.8% and with
authentication accuracy average of 96.9%. Regarding segment processing case, the accuracy is nearly the
same to the processing of the whole signal case; but the time required for authentication was faster in the
segmantion situation. To over come this issue, we have processed the whole signal as a series of segments; so
we gain the high accuracy rate along with speed in performance.
We have done a compersion between our method and previous related wroks in term of number of
subjects, false accepted rate and accuracy matters like in Table 1. It is obvious that we have increased the
authentication accuracy rate and decreased the false acceptance rate.
Table 1. Authentication Result of Mothods
Recerch No.
Subjects
Year FAR Accuracy
W. Yarong et al. [6] 15 2015 3.61% 96.4%
J. S. Arteaga-Falconi et al. [1] 73 2016 1.29% 84.93%
Our propoused method 44 2017 1.21% 97.2%
5. CONCLUSION
Our design paves the way towards a comprehensive secure system that is using a new feature which
is ECG signal analysis as one of the biometric methods. By using ECG signal waveform features as biometric
authentication we have concluded that these features cannot be copied or manipulated. Therefore, systems
immunity against intrusion and fake signature to identify the person during the authentication process will be
impervious.
We have increased the accuracy rate and reduced the false accept rate significantly, comparing with
previoues related work like in [1, 6] cases. By reducing the false accepted rate, the authentication accuracy
will be steadily increased. We have successfully reached a high accuracy level in authentication and
classification wise of ECG raw signal. We are looking forward to deploy my system effectively in many life
aspects like health monitoring; not just regarding systems security matters.
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BIOGRAPHIES OF AUTHORS
Ahmed Younes Shdefat received the B.E. degree in computer information system from Yarmouk
University, Jordan, in 2008 and the M.E. degree in information technology from university Utara
Malaysia (UUM), Malaysia, in 2010. His previous position was a university lecturer at Computer
Department – College of community service – Taibah University, KSA and now a lecturer at the
Department of Information System and Technology, American University of Middle East, Kuwait.
He also pursues for Ph.D. at Department of Computer Engineering, Inje University, Korea. He has
interests in the areas of bio-signals, software engineering, and data mining. He has published several
papers in bio-signals.
Moon-Il Joo received the MS degree in computer engineering from Inje University, Korea, in 2012.
He currently studies for PhD at the department of computer science, Inje University, Korea He has
interests in the areas of software engineering, human computer interaction, smart phone
programming, and component based development.
8. Int J Elec & Comp Eng ISSN: 2088-8708
Utilizing ECG Waveform Features as New Biometric Authentication Method (Ahmed Younes Shdefat)
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Sung-Hoon Choi received the BS degree in computer science from Inje University, Korea, in 2016.
He is currently a graduate student at the Institute of Digital Anti-Aging Healthcare (IDA), Inje
University, Korea. He has interests in the areas of software engineering, artificial intelligence and
healthcare software.
Hee-Cheol Kim received the MS degree in computer science from SoGang University, Korea, in
1991, and the PhD in computer science from Stockholm University / Royal Institute of Technology,
Sweden in 2001. He is a professor at Institute of Digital Anti-Aging Healthcare (IDA) / Department
of Computer Engineering / Smart Wellness Lab, Inje University, Korea. He has been involved as
coordinator/director in research projects including the project of „Development of nanofiber-based
wellness wear system‟ funded by the ministry of knowledge and economy ($ 2 million, 2009. 6. –
2014. 5.) He has interests in the areas of human computer interaction, software engineering, and
digital healthcare. He has also published more than 100 papers in these areas.