This document presents a new algorithm for estimating pulse transit time (PTT) using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. The algorithm detects peaks and feet in the PPG signal and R peaks in the ECG signal. PTT is calculated as the time difference between an ECG R peak and the corresponding PPG peak or foot. The algorithm was tested on data from 37 subjects and achieved average sensitivities of 97.5% and 97.77% for R peak and PPG peak detection, and accuracies of 96.82% and 97.64% respectively. Sensitivity and accuracy for PPG foot detection were 98.33% and 94.14%
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.
Methodology for detection of paroxysmal atrial fibrillation based on P-Wave, ...IJECEIAES
The detection of paroxysmal atrial fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast fourier transform (FFT), Bayes optimal classifier (BOC), K-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or at most two of them.
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.
Algorithm for the Representation of Parameter Values of ElectrocardiogramTELKOMNIKA JOURNAL
This document presents an algorithm for representing parameter values from electrocardiogram (ECG) data. The algorithm analyzes discrete ECG data to determine peak values (P, Q, R, S, T) in each cycle as well as segment and interval durations. It uses the maximum R peak amplitude as a threshold to identify cycles. The start and end points of each cycle are calculated relative to the R peak of that cycle. The algorithm then identifies the maximum and minimum amplitudes within each cycle to determine the peak values. It outputs the parameter values for each lead cycle. The algorithm allows rapid determination of ECG parameters without manual analysis and can speed diagnosis of heart conditions.
This document proposes a policy-based runtime verification framework for hypertension monitoring using electrocardiogram (ECG) sensing. Key aspects include:
1) A decision tree model is implemented using timed ECG features to extract patterns/policies related to hypertension.
2) The extracted ECG policies are formally specified as timed automata to synthesize a runtime verification monitor.
3) The monitor continuously verifies the ECG policies and provides a verdict on whether hypertension is present or not based on ECG events.
The framework aims to provide explainable, non-invasive hypertension monitoring using a formal methods-based approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Reconstruction of Respiratory Signal from ECGIRJET Journal
This document proposes and evaluates methods to reconstruct respiratory signals from electrocardiogram (ECG) recordings. It examines using features of the ECG signal such as R-R intervals, R-peak amplitudes, P-peak amplitudes, T-peak amplitudes, and Q and S valley amplitudes. It also examines using wavelet decomposition and empirical mode decomposition to extract respiratory information from ECG recordings. The document tests the proposed methods on ECG recordings from healthy subjects and databases. It analyzes the reconstructed respiratory signals compared to actual simultaneously recorded respiratory signals. The results show correlations from 0.5 to 0.9 and mean errors from 0.2 to 2.8 breaths per minute depending on the method and database.
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.
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.
Methodology for detection of paroxysmal atrial fibrillation based on P-Wave, ...IJECEIAES
The detection of paroxysmal atrial fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast fourier transform (FFT), Bayes optimal classifier (BOC), K-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or at most two of them.
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.
Algorithm for the Representation of Parameter Values of ElectrocardiogramTELKOMNIKA JOURNAL
This document presents an algorithm for representing parameter values from electrocardiogram (ECG) data. The algorithm analyzes discrete ECG data to determine peak values (P, Q, R, S, T) in each cycle as well as segment and interval durations. It uses the maximum R peak amplitude as a threshold to identify cycles. The start and end points of each cycle are calculated relative to the R peak of that cycle. The algorithm then identifies the maximum and minimum amplitudes within each cycle to determine the peak values. It outputs the parameter values for each lead cycle. The algorithm allows rapid determination of ECG parameters without manual analysis and can speed diagnosis of heart conditions.
This document proposes a policy-based runtime verification framework for hypertension monitoring using electrocardiogram (ECG) sensing. Key aspects include:
1) A decision tree model is implemented using timed ECG features to extract patterns/policies related to hypertension.
2) The extracted ECG policies are formally specified as timed automata to synthesize a runtime verification monitor.
3) The monitor continuously verifies the ECG policies and provides a verdict on whether hypertension is present or not based on ECG events.
The framework aims to provide explainable, non-invasive hypertension monitoring using a formal methods-based approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Reconstruction of Respiratory Signal from ECGIRJET Journal
This document proposes and evaluates methods to reconstruct respiratory signals from electrocardiogram (ECG) recordings. It examines using features of the ECG signal such as R-R intervals, R-peak amplitudes, P-peak amplitudes, T-peak amplitudes, and Q and S valley amplitudes. It also examines using wavelet decomposition and empirical mode decomposition to extract respiratory information from ECG recordings. The document tests the proposed methods on ECG recordings from healthy subjects and databases. It analyzes the reconstructed respiratory signals compared to actual simultaneously recorded respiratory signals. The results show correlations from 0.5 to 0.9 and mean errors from 0.2 to 2.8 breaths per minute depending on the method and database.
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.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Journals
Abstract Photoplethysmography is a non-invasive electro-optic method developed by Hertzman, which provides information on the blood volume flowing at a particular test site on the body close to the skin. PPG waveform contains two components; one, attributable to the pulsatile component in the vessels, i.e. the arterial pulse, which is caused by the heartbeat, and gives a rapidly alternating signal (AC component). The second one is due to the blood volume and its change in the skin which gives a steady signal that changes very slowly (DC component). PPG signal consists of not only the heart-beat information but also a respiratory signal. Estimation of respiration rates from Photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information.. There have been several efforts on PPG Derived Respiration (PDR), these methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals. PCA identifies patterns in data, and expresses the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing such data. Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications. Test results on PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method. Index Terms: Respiratory signal, PPG signal, Principal Component Analysis, EMD, ECG
Rule Based Identification of Cardiac Arrhythmias from Enhanced ECG Signals Us...CSCJournals
The detection of abnormal cardiac rhythms, automatic discrimination from rhythmic heart activity, became a thrust area in clinical research. Arrhythmia detection is possible by analyzing the electrocardiogram (ECG) signal features. The presence of interference signals, like power line interference (PLI), Electromyogram (EMG) and baseline drift interferences, could cause serious problems during the recording of ECG signals. Many a time, they pose problem in modern control and signal processing applications by being narrow in-band interference near the frequencies carrying crucial information. This paper presents an approach for ECG signal enhancement by combining the attractive properties of principal component analysis (PCA) and wavelets, resulting in multi-scale PCA. In Multi-Scale Principal Component Analysis (MSPCA), the PCA’s ability to decorrelate the variables by extracting a linear relationship and wavelet analysis are utilized. MSPCA method effectively processed the noisy ECG signal and enhanced signal features are used for clear identification of arrhythmias. In MSPCA, the principal components of the wavelet coefficients of the ECG data at each scale are computed first and are then combined at relevant scales. Statistical measures computed in terms of root mean square deviation (RMSD), root mean square error (RMSE), root mean square variation (RMSV) and improvement in signal to noise ratio (SNRI) revealed that the Daubechies based MSPCA outperformed the basic wavelet based processing for ECG signal enhancement. With enhanced signal features obtained after MSPCA processing, the detectable measures, QRS duration and R-R interval are evaluated. By using the rule base technique, projecting the detectable measures on a two dimensional area, various arrhythmias are detected depending upon the beat falling into particular place of the two dimensional area.
IRJET- R–Peak Detection of ECG Signal using Thresholding MethodIRJET Journal
This document presents a method for detecting R-peaks in an electrocardiogram (ECG) signal using thresholding to measure heart rate. The method analyzes ECG data from the MIT-BIH Arrhythmia Database using MATLAB. It detects R-peaks by applying amplitude thresholds to identify peaks above neighboring samples and a minimum amplitude. Detected R-peaks are used to calculate the average RR interval and classify heart rate as normal, bradycardia (slow), or tachycardia (fast). The method is tested on several ECG records and can approximate results quickly but has limitations and is not intended for diagnosis due to potential missed detections of flattened R-peaks.
IRJET- Detection of Atrial Fibrillation by Analyzing the Position of ECG Sign...IRJET Journal
This document presents a method for detecting atrial fibrillation by analyzing electrocardiogram (ECG) signals. The method involves denoising the ECG signal using a Savitzky-Golay filter and wavelet transformation. Q-peaks are detected from the transformed signal and the interval between Q-peaks is used to identify atrial fibrillation, with intervals less than 0.6 seconds indicating atrial fibrillation. Cross-correlation of the ECG signal is also used to distinguish between healthy and unhealthy signals. The method is tested on normal ECG signals and signals with atrial fibrillation, correctly identifying the conditions based on the Q-peak interval and shape of the cross-correlation.
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.
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 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.
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.
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
Identification of Myocardial Infarction from Multi-Lead ECG signalIJERA Editor
Electrocardiogram (ECG) is the cheap and noninvasive method of depicting the heart activity and abnormalities.
It provides information about the functionality of the heart. It is the record of variation of bioelectric potential
with respect to time as the human heart beats. The classification of ECG signals is an important application since
the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through
appropriate treatment. Since the ECG signals, while recording are contaminated by several noises it is necessary
to preprocess the signals prior to classification. Digital filters are used to remove noise from the signal. Principal
component analysis is applied on the 12 lead signal to extract various features. The present paper shows the
unique feature, point score calculated on the basis of the features extracted from the ECG signal. The point
score calculation is tested for 40 myocardial infarction ECG signals and 25 Normal ECG signals from the PTB
Diagnostic database with 94% sensitivity.
ECG Signal Analysis for Myocardial Infarction DetectionUzair Akbar
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
P-Wave Related Disease Detection Using DWTIOSRJVSP
: ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. This paper focuses on detection of the P-wave, based on 12 lead standard ECG, which will be applied to the detection of patients prone to diseases. The ECG signal contains noise and that noise is removed using Bandpass filter. After elimination of noise, we detect QRS complex which help in detecting the P-Wave. P-wave morphology can be determined in leads II as monophasic and V1 as biphasic during sinus rhythm. DWT provides a value that helps in estimating features of the P-Wave. This detects the diseases that occur when the P-wave is abnormal. The method has been validated using ECG recordings of 250 patients with a wide variety of P-wave morphologies from Database
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.
Ecg based heart rate monitoring system implementation using fpga for low powe...eSAT Journals
Abstract This paper proposes a new design to monitor the Heart Rate from Electrocardiogram (ECG) signal. The proposed design is based on the concept of identifying the voltage level of the R-wave complex component of the ECG signal above a threshold level. A 100 Hertz sample rate is selected to sample the complex ECG signal. A dual-counter based calculation method is used to obtain the mathematical value of Heart Rate. The proposed FPGA based ECG Heart Rate monitoring system can operate with high performance with respect to the low-power and high speed. The system is designed using Verilog hardware design language and Xilinx XC3s500E FPGA. Keywords- ECG, Sampling, Threshold, Heart Rate, FPGA
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- Study of Hypocalcemic Cardiac Disorder by Analyzing the Features o...IRJET Journal
This document presents a study that analyzes ECG signals using discrete wavelet transform (DWT) to detect hypocalcemia, a condition caused by low calcium levels. The proposed methodology involves denoising the ECG signal, detecting peaks (Q, R, S) using DWT, calculating time intervals, and using statistical measures like mean square error, root mean square deviation, and percentage deviation to distinguish between healthy and hypocalcemic patients. The results of applying this methodology to ECG signals from a database are discussed.
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.
IRJET- R Peak Detection with Diagnosis of Arrhythmia using Adaptive Filte...IRJET Journal
The document presents a method for detecting R peaks in electrocardiogram (ECG) signals with high accuracy by combining adaptive filtering and Hilbert transform. Adaptive filtering reduces noise and estimates the fundamental signal, while Hilbert transform eliminates signal distortion and shows time dependency. Features are then extracted from the ECG, including RR interval, heart rate, QRS width, and PR interval. These features can be used to diagnose arrhythmias based on irregular heart rhythms. A graphical user interface was also developed to conveniently display the output waveform, features, and type of arrhythmia diagnosis. When tested on data from the MIT-BIH arrhythmia database, the proposed method achieved a sensitivity of 99.22% and positive predict
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.
Call for Research Articles - 10th International Conference on Bioinformatics ...bioejjournal
10th International Conference on Bioinformatics & Biosciences (BIOS 2024) is a forum for presenting new advances and research results in the field of biology to increase the understanding of all biological process. The aim of this conference is to publish all the latest and outstanding research articles in all areas of bioinformatics and Biometrics. Researchers and scientists from the fields of biology, computer science, mathematics, statistics, and physics are invited to share their developments and new techniques in the fields of Biometrics and Bioinformatics. .
DE NOVO TRANSCRIPTOME ASSEMBLY OF SOLID SEQUENCING DATA IN CUCUMIS MELObioejjournal
As sequencing technologies progress, focus shifts towards solving bioinformatic challenges, of which
sequence read assembly is the first task. In the present study, we have carried out a comparison of two
assemblers (SeqMan and CLC) for transcriptome assembly, using a new dataset from Cucumis melo.
Between two assemblers SeqMan generated an excess of small, redundant contigs where as CLC generated
the least redundant assembly. Since different assemblers use different algorithms to build contigs, we
followed the merging of assemblies by CAP3 and found that the merged assembly is better than individual
assemblies and more consistent in the number and size of contigs. Combining the assemblies from different
programs gave a more credible final product, and therefore this approach is recommended for quantitative
output
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Extraction of respiratory rate from ppg signals using pca and emdeSAT Journals
Abstract Photoplethysmography is a non-invasive electro-optic method developed by Hertzman, which provides information on the blood volume flowing at a particular test site on the body close to the skin. PPG waveform contains two components; one, attributable to the pulsatile component in the vessels, i.e. the arterial pulse, which is caused by the heartbeat, and gives a rapidly alternating signal (AC component). The second one is due to the blood volume and its change in the skin which gives a steady signal that changes very slowly (DC component). PPG signal consists of not only the heart-beat information but also a respiratory signal. Estimation of respiration rates from Photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information.. There have been several efforts on PPG Derived Respiration (PDR), these methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals. PCA identifies patterns in data, and expresses the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing such data. Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications. Test results on PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method. Index Terms: Respiratory signal, PPG signal, Principal Component Analysis, EMD, ECG
Rule Based Identification of Cardiac Arrhythmias from Enhanced ECG Signals Us...CSCJournals
The detection of abnormal cardiac rhythms, automatic discrimination from rhythmic heart activity, became a thrust area in clinical research. Arrhythmia detection is possible by analyzing the electrocardiogram (ECG) signal features. The presence of interference signals, like power line interference (PLI), Electromyogram (EMG) and baseline drift interferences, could cause serious problems during the recording of ECG signals. Many a time, they pose problem in modern control and signal processing applications by being narrow in-band interference near the frequencies carrying crucial information. This paper presents an approach for ECG signal enhancement by combining the attractive properties of principal component analysis (PCA) and wavelets, resulting in multi-scale PCA. In Multi-Scale Principal Component Analysis (MSPCA), the PCA’s ability to decorrelate the variables by extracting a linear relationship and wavelet analysis are utilized. MSPCA method effectively processed the noisy ECG signal and enhanced signal features are used for clear identification of arrhythmias. In MSPCA, the principal components of the wavelet coefficients of the ECG data at each scale are computed first and are then combined at relevant scales. Statistical measures computed in terms of root mean square deviation (RMSD), root mean square error (RMSE), root mean square variation (RMSV) and improvement in signal to noise ratio (SNRI) revealed that the Daubechies based MSPCA outperformed the basic wavelet based processing for ECG signal enhancement. With enhanced signal features obtained after MSPCA processing, the detectable measures, QRS duration and R-R interval are evaluated. By using the rule base technique, projecting the detectable measures on a two dimensional area, various arrhythmias are detected depending upon the beat falling into particular place of the two dimensional area.
IRJET- R–Peak Detection of ECG Signal using Thresholding MethodIRJET Journal
This document presents a method for detecting R-peaks in an electrocardiogram (ECG) signal using thresholding to measure heart rate. The method analyzes ECG data from the MIT-BIH Arrhythmia Database using MATLAB. It detects R-peaks by applying amplitude thresholds to identify peaks above neighboring samples and a minimum amplitude. Detected R-peaks are used to calculate the average RR interval and classify heart rate as normal, bradycardia (slow), or tachycardia (fast). The method is tested on several ECG records and can approximate results quickly but has limitations and is not intended for diagnosis due to potential missed detections of flattened R-peaks.
IRJET- Detection of Atrial Fibrillation by Analyzing the Position of ECG Sign...IRJET Journal
This document presents a method for detecting atrial fibrillation by analyzing electrocardiogram (ECG) signals. The method involves denoising the ECG signal using a Savitzky-Golay filter and wavelet transformation. Q-peaks are detected from the transformed signal and the interval between Q-peaks is used to identify atrial fibrillation, with intervals less than 0.6 seconds indicating atrial fibrillation. Cross-correlation of the ECG signal is also used to distinguish between healthy and unhealthy signals. The method is tested on normal ECG signals and signals with atrial fibrillation, correctly identifying the conditions based on the Q-peak interval and shape of the cross-correlation.
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.
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 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.
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.
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
Identification of Myocardial Infarction from Multi-Lead ECG signalIJERA Editor
Electrocardiogram (ECG) is the cheap and noninvasive method of depicting the heart activity and abnormalities.
It provides information about the functionality of the heart. It is the record of variation of bioelectric potential
with respect to time as the human heart beats. The classification of ECG signals is an important application since
the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through
appropriate treatment. Since the ECG signals, while recording are contaminated by several noises it is necessary
to preprocess the signals prior to classification. Digital filters are used to remove noise from the signal. Principal
component analysis is applied on the 12 lead signal to extract various features. The present paper shows the
unique feature, point score calculated on the basis of the features extracted from the ECG signal. The point
score calculation is tested for 40 myocardial infarction ECG signals and 25 Normal ECG signals from the PTB
Diagnostic database with 94% sensitivity.
ECG Signal Analysis for Myocardial Infarction DetectionUzair Akbar
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
P-Wave Related Disease Detection Using DWTIOSRJVSP
: ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. This paper focuses on detection of the P-wave, based on 12 lead standard ECG, which will be applied to the detection of patients prone to diseases. The ECG signal contains noise and that noise is removed using Bandpass filter. After elimination of noise, we detect QRS complex which help in detecting the P-Wave. P-wave morphology can be determined in leads II as monophasic and V1 as biphasic during sinus rhythm. DWT provides a value that helps in estimating features of the P-Wave. This detects the diseases that occur when the P-wave is abnormal. The method has been validated using ECG recordings of 250 patients with a wide variety of P-wave morphologies from Database
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.
Ecg based heart rate monitoring system implementation using fpga for low powe...eSAT Journals
Abstract This paper proposes a new design to monitor the Heart Rate from Electrocardiogram (ECG) signal. The proposed design is based on the concept of identifying the voltage level of the R-wave complex component of the ECG signal above a threshold level. A 100 Hertz sample rate is selected to sample the complex ECG signal. A dual-counter based calculation method is used to obtain the mathematical value of Heart Rate. The proposed FPGA based ECG Heart Rate monitoring system can operate with high performance with respect to the low-power and high speed. The system is designed using Verilog hardware design language and Xilinx XC3s500E FPGA. Keywords- ECG, Sampling, Threshold, Heart Rate, FPGA
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- Study of Hypocalcemic Cardiac Disorder by Analyzing the Features o...IRJET Journal
This document presents a study that analyzes ECG signals using discrete wavelet transform (DWT) to detect hypocalcemia, a condition caused by low calcium levels. The proposed methodology involves denoising the ECG signal, detecting peaks (Q, R, S) using DWT, calculating time intervals, and using statistical measures like mean square error, root mean square deviation, and percentage deviation to distinguish between healthy and hypocalcemic patients. The results of applying this methodology to ECG signals from a database are discussed.
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.
IRJET- R Peak Detection with Diagnosis of Arrhythmia using Adaptive Filte...IRJET Journal
The document presents a method for detecting R peaks in electrocardiogram (ECG) signals with high accuracy by combining adaptive filtering and Hilbert transform. Adaptive filtering reduces noise and estimates the fundamental signal, while Hilbert transform eliminates signal distortion and shows time dependency. Features are then extracted from the ECG, including RR interval, heart rate, QRS width, and PR interval. These features can be used to diagnose arrhythmias based on irregular heart rhythms. A graphical user interface was also developed to conveniently display the output waveform, features, and type of arrhythmia diagnosis. When tested on data from the MIT-BIH arrhythmia database, the proposed method achieved a sensitivity of 99.22% and positive predict
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.
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discriminant function. The results showed that PCG with a 19 dB Signal-to-Noise-Ratio can lead to 97.33%
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structure, gene, functional structure analysis which help medical staff detect cancer, which in turn can help
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the most common occurring malignancies in the world, especially in India where the prevalence for
smoking, Areca nut chewing coupled with a lifestyle that encourages these two activities as fashion are left
many people diagnosed with OSCC. Patients with this OSCC are more likely unaware of its side effects
and over time might suffer from facial deformity. The importance to understanding the symptoms,
prevention and treatment of oral cancer is very much essential today. In this paper, we looked at over 2000
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have highlighted NGS role in OSCC Diagnosis. We did like to see a comprehensive review on the papers
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cancer, also known as Squamous Cell Carcinoma (OSCC). Early detection leads to better survival rate,
therefore, education on yearly check-ups plays a vital role. Computational analysis at the genomic (DNA
sequence) can help patients with targeted cellular treatment and hopefully a cure. In this paper, we would
look at computation tools used in detecting OSCC and various analysis. Analysis includes detecting
abnormality in the cell and other molecular reactions which later morph into a cancerous cell. Later, we
investigate all computational tools or techniques from local and global sequence alignment, protein
structure, gene, functional structure analysis which help medical staff detect cancer, which in turn can help
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Statistical Based Media Optimization and Production of Clavulanic Acid By Sol...bioejjournal
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A New Low-Complexity Algorithm for the Pulse Transit Time Evaluationbioejjournal
The pulse transit time (PTT) is a physiological parameter commonly derived from Electrocardiogram
(ECG) and Photoplethysmogram (PPG) signal. It is defined as the time taken for the arterial pulse to
travel from the heart to a peripheral site, and can be used as a direct indicator of Cardiovascular Diseases
(CVD). In this study, we propose a new low-complexity algorithm for the (PTT) estimation. The (PTT) is
calculated as the interval between the peak of the ECG R-wave and a time point on the PPG. We
considered a dataset of 37 subjects containing a simultaneous recording of the (ECG) and the (PPG). The
computation of the (PTT) consists of detecting the peak and foot points of a (PPG) and the R peak of the
(ECG) signal. Our algorithm is improved by a temporal analysis by windowing. The results obtained are
promising. The average sensitivity (SEN) and accuracy (ACC) obtained are respectively (97.5%, and
96.82%) for the detection of R peaks and (97.77%, and 97.64%) for the detection of PPG peaks. The
sensitivity (SEN) and accuracy (ACC) of the foot (PPG) detection were 98.33% and 94.14%.
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A NEW LOW-COMPLEXITY ALGORITHM FOR THE PULSE TRANSIT TIME EVALUATION
1. Bioscience & Engineering: An International Journal (BIOEJ), April 2023, Volume 10, Number 1/2
DOI: 10.5121/bioej.2023.10202 15
A NEW LOW-COMPLEXITY ALGORITHM FOR THE
PULSE TRANSIT TIME EVALUATION
Radjef Lilia and Omari Tahar
Department of Electrical Engineering Systems, Boumerdes University,
Boumerdes, Algeria
ABSTRACT
The pulse transit time (PTT) is a physiological parameter commonly derived from Electrocardiogram
(ECG) and Photoplethysmogram (PPG) signal. It is defined as the time taken for the arterial pulse to
travel from the heart to a peripheral site, and can be used as a direct indicator of Cardiovascular Diseases
(CVD). In this study, we propose a new low-complexity algorithm for the (PTT) estimation. The (PTT) is
calculated as the interval between the peak of the ECG R-wave and a time point on the PPG. We
considered a dataset of 37 subjects containing a simultaneous recording of the (ECG) and the (PPG). The
computation of the (PTT) consists of detecting the peak and foot points of a (PPG) and the R peak of the
(ECG) signal. Our algorithm is improved by a temporal analysis by windowing. The results obtained are
promising. The average sensitivity (SEN) and accuracy (ACC) obtained are respectively (97.5%, and
96.82%) for the detection of R peaks and (97.77%, and 97.64%) for the detection of PPG peaks. The
sensitivity (SEN) and accuracy (ACC) of the foot (PPG) detection were 98.33% and 94.14%.
KEYWORDS
Pulse transit time (PTT), Cardiovascular Disease (CVD), Electrocardiogram (ECG),
Photoplethysmography (PPG), Algorithm, Peaks detection, Sensitivity (SEN), Accuracy (ACC).
1. INTRODUCTION
Cardiovascular disease is the leading cause of death in the world, according to the World Health
Organization (WHO). The early detection of vascular diseases can be the key to effective
prevention and treatment [1, 2, 3]. Pulse transit time (PTT) provides information from the arterial
tree on arterial stiffness (AS) [4], vessel compliance, and blood pressure (BP) [5, 6, 7, 8]. The
pulse transit time (PTT) is defined as the time taken for the pulse wave to travel from a proximal
point to a distal point in the arterial system. It is based on the Moens-Koertweg and Bramwell-
Hill equations [9] and is inversely related to the pulse wave velocity (PWV), which is calculated
as the distance traveled by the pulse wave divided by time. The use of (PTT) dates back to 1964
when Weltman et al [10] designed the PWV computer based on the use of the (ECG) complex
and a downstream pulse signal to determine the pulse transit time over a known arterial length.
The (PTT) can be measured using pulse wave transducers placed close together in a
homogeneous arterial segment [11] (as shown in Figure 1).
2. Bioscience & Engineering: An International Journal (BIOEJ), April 2023, Volume 10, Number 1/2
16
Figure 1. The pulse transit time (PTT)
There are different non-invasive techniques to measure (PTT), such as arterial tonometry, doppler
ultrasound, electrocardiography (ECG signal represents the electrical activity of the heart) -
photoplethysmography (PPG signal measures changes in blood volume), and pressure transducers
[12, 13, 14,15,16]. The (PTT) techniques are reproducible, non-invasive, easy, and safe, it is
therefore not necessary for specialized training required for medical staff to handle them.
Depending on the equipment used and the applications, the (PTT) can be defined as the time
difference between the onset of cardiac ejection approximated by the R-peak in the
electrocardiogram (ECG) and the arrival of the pulse at the fingertip as determined by the
photoplethysmogram (PPG) [17, 18, 19, 20, 21, 22] (shown in Figure 2). Promising applications
of (PTT) include the detection of stroke and myocardial infarction [23], sleep-disordered
breathing [24], monitoring of ductus arteriosus closure in neonates [25], detection of sympathetic
nervous system (SNS) excitation [26], etc. The evolution of (PTT) is related to changes in the
cardiovascular system. For example, changes in systolic blood pressure (SBP) and/or arterial
stiffness (AS) [27].
Figure 2. Graphical explanation of the (PTT) calculation using (PPG) and (ECG) signals
A combination of the (ECG) and (PPG) signals leads to the measurement of another
cardiovascular parameter called pulse arrival time (PAT). The (PAT) includes not only the
desired (PTT) but also a rejection period (PEP). This approach has been extensively reported in
the literature [28, 29, 30]. Another approach (2), the (PTT) can be acquired by observing two
(PPG) waves distant from each other [31,32], or by using only one (PPG) signal [33, 34, 35, 36],
different measurement sites exist in the periphery including the finger, ear lobe, toe, and forehead
although they are less practical. To measure the (PTT) (or PAT), various vital signals such as
Photoplethysmograph (PPG), electrocardiogram (ECG), ballistocardiogram (BCG),
3. Bioscience & Engineering: An International Journal (BIOEJ), April 2023, Volume 10, Number 1/2
17
gyrocardiography (GCG), impedance plethysmography (IPG), electrical bio-impedance (Bimp),
the PPG/tonoarteriogram (TAG), impedance cardiography (ICG) and seismocardiogram (SCG)
can be used [37]. The features obtained from the (ECG) and (PPG) signals depend on the purpose
or type of disease and diagnosis to be estimated. In the literature, several algorithms based on
characteristics of the pulse waveform analysis have been proposed, mainly focusing on the
determination of the characteristic point’s peak detection [38, 39, 40, 41], and located at the foot
of the wave. The robust determination of characteristic points is still a difficult task in the (PTT)
estimation due to motion artifacts, electrical interference noises, and signal crossovers among
others, and also due to respiration. This article presents a new algorithm for non-invasive
measurements of pulse transit time (PTT), obtained by measuring the pulse time between the
heart and the finger. The (PTT-Peak) and (PTT-Foot) are the time delays between the peak of the
wave (ECG-R) and the peak and foot (PPG), respectively.
2. METHODOLOGY
The (ECG) and (PPG) signals were processed to measure the (PTT), which is estimated using the
algorithm illustrated in Figure 3. The (PTT-foot and PTT-peak) values are obtained by the
measurement of the differences between the (PPG) (foot, peak) locations and R-peak locations.
2.1. Training Dataset
First, we constructed a data set of 37 subjects containing a simultaneous recording of (ECG) and
(PPG). All subjects signed a voluntary participation agreement for this study. Approval for data
acquisition was granted by the ethics committee of the University of Tlemcen.
2.2. The PTT Algorithm
First, the (PPG) is normalized at the value of 1 according to the equation (1):
P P G (normalized) = (P P G .(n)/ (max (P P G)) (1)
Where n: is the normalization factor. In our case, it equals 1.
The peaks (PPG) were detected using a thresholding operation (a threshold of 0.5). To detect
local maxima and minima, the first derivative was calculated and thresholded symmetrically
(+0.5 and -0.5). Subtracting each peak location (in the PPG signal) with the difference between
its minima and maxima location (in the derivative signal) perfectly detects the (PPG-foot). The
(PPG-foot) detection process evolved mathematically from a Gaussian pulse (which strongly
corresponds to a (PPG) pulse) and its first derivative, all steps (shown in Figure 4). Signal
processing (ECG) begins with normalization (to the value of 1) followed by a thresholding
operation (to the value of 0.3) to detect peaks R. A threshold value is set to less than 1% of the
pulse value. The threshold value is set to less than 50% of the normalized signal to avoid any loss
in detection, as well as to avoid misleading detection of R-peaks resulting in some cases from
large amplitude T-waves. The algorithm is improved by a temporal analysis by windowing, from
which the maximum value in each window perfectly locates the R peak, (as shown in Figure 5).
4. Bioscience & Engineering: An International Journal (BIOEJ), April 2023, Volume 10, Number 1/2
18
Start
Recording the (ECG) and (PPG) signals
or (ECG) and (PPG) Database
Raw (PPG) Raw (ECG)
Normalization Normalization
Segments containing both
waveforms simultaneously
Features extraction
ECG PPG
Thresholding
Operation Thresholding
Operation
R-Peak Detection
PPG-Peak Detection
Outlier
Removal PPG-Peak
Improvement of R-
Peak Detection
ECG-Peak
First Derivative
Positive
Thresholding
Positive
Thresholding
Maxs
Detection
Mins
Detection
Diff= Max-Min
PTT- foot
R-Peak-PPG-foot
PTT- Peak
R-Peak-PPG-Peak
End
Location of Pulse foot
PPG-foot = Peak-Diff
Figure 3. The algorithm developed for the (PTTs) (PTT-f and PTT-p) detection
5. Bioscience & Engineering: An International Journal (BIOEJ), April 2023, Volume 10, Number 1/2
19
3. RESULTS
Figure 4. Localization result of (PPG-foot) and (PPG-peak). (a): normalization and thresholding operation,
(b): (PPG-Peak) detection, (c): the first derivation of (PPG) signal and detection of local maxima and
minima, and (d): (PPG-foot) localization
Figure 5. (ECG) processing and measurement of (PTT) foot and peak, (a): normalization, thresholding
operation, and all local maxima detection, (b): improvement of R-peak detection, (c): measurement of
(PTT-foot), and (PTT-peak)
6. Bioscience & Engineering: An International Journal (BIOEJ), April 2023, Volume 10, Number 1/2
20
4. DISCUSSION
Experimental results of the proposed algorithm are evaluated in terms of sensitivity (SEN) and
accuracy (ACC) given by equations (2) and (3), respectively. Where TP (true positive) is the
number of peaks (or feet) correctly recognized, FN (false negative) is the number of peaks (or
feet) missed, and FP (false positive) is the number of false peaks (or feet) recognized as true.
SEN = T P/(T P + F N) ×100% (2)
ACC = T P/(T P + F N + F P) ×100% (3)
Where TP (true positive) is the number of peaks (or feet) correctly recognized, FN (false
negative) is the number of peaks (or feet) missed, and FP (false positive) is the number of false
peaks (or feet) recognized as true. Obtained results show satisfactory performances on the
records. We note that only the correct detections are used in this study.
Table 1 shows the accuracy and sensitivity values of the algorithm. The total beats recorded over
all subjects were 719 beats with an average of 24±9 beats. In the case of R-peak detection (ECG-
p), the algorithm fails to detect 23 beats (18 FN beats and 5 FP beats) out of 719 beats. The
average SEN and ACC of R peaks detection were 97.5%, and 96.82% respectively. In the case of
PPG-peak detection, the algorithm fails to detect 17 beats (16 FN beats and 1 FP beat) out of 719
beats. The average (SEN) and (ACC) of PPG-peak detection were 97.77%, and 97.64%,
respectively. In the case of PPG foot detection, the algorithm mislocated 54 beats (12 FN beats
and 32 FP beats) out of 719 beats. The average (SEN) and (ACC) of (PPG- foot) detection were
98.33%, and 94.14%, respectively.
Table 1. Detection results of the algorithm.
Total beats=719(Avg=24±9 beats)
TP FN FP Accuracy% Sensitivity%
ECG-p 701 18 5 96.82 97.50
PPG-p 703 16 1 97.64 97.77
PPG-f 707 12 32 94.14 98.33
5. CONCLUSIONS
In this paper, a new algorithm for the estimation of (PTT) was introduced. The (PTT) is a
parameter of major importance in the prevention of cardiovascular diseases, especially arterial
aging and hypertension. For the estimation of (PTT), the collected data were processed. Using the
(ECG) and (PPG) signals, we obtained the (PTT- foot) and (PTT- peak). A good result was found
by evaluating several statistical measures.
7. Bioscience & Engineering: An International Journal (BIOEJ), April 2023, Volume 10, Number 1/2
21
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9. Bioscience & Engineering: An International Journal (BIOEJ), April 2023, Volume 10, Number 1/2
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AUTHOR
Radjef Lilia PhD student in Biomedical Instrumentation at M'hamed BOUGARA
University of Boumerdes, Department of Electrical Systems Engineering Member of
the Biomedical Instrumentation Laboratory (LIB) research team