The document analyzes epilepsy using the Approximate Entropy (ApEn) algorithm. ApEn is used to measure irregularity and unpredictability in EEG signals. The study applies the ApEn algorithm to EEG data from 4 subjects - 2 non-epileptic and 2 epileptic. For each subject, EEG data was recorded under different conditions, such as eyes open/closed, during seizures, etc. Integrated ApEn waveforms were generated for 4 selected data sets to analyze and distinguish epileptic from non-epileptic EEG signals. The results showed that the integrated ApEn waveform had minimal variation for the non-epileptic subject with eyes closed, and slightly more variation for the non-epileptic subject with eyes
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
Ecg beat classification and feature extraction using artificial neural networ...priyanka leenakhabiya
This document discusses a proposed technique for ECG beat classification and feature extraction using artificial neural networks and discrete wavelet transform. The key steps of the proposed technique include ECG data pre-processing using discrete wavelet transform to remove noise, extracting features such as RR interval and QRS complex, designing and training an artificial neural network on the extracted features, and using an Euclidean classifier to classify different ECG cases based on the minimum distance between features. Experimental results on ECG data from the MIT-BIH database show that the proposed technique achieves high classification accuracy and sensitivity compared to previous methods.
Classification of ecg signal using artificial neural networkGaurav 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).
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).
Epileptic Seizure Detection using An EEG SensorIRJET Journal
This document presents a method for detecting epileptic seizures using an EEG sensor and signal processing techniques. It involves using an EEG headset to record raw brain wave data, filtering the signals to remove noise, applying discrete wavelet transform to extract features from different frequency bands, and using a support vector machine classifier to classify segments as normal, interictal, or ictal based on the extracted features. The proposed method aims to help doctors more accurately diagnose and monitor epilepsy in patients by objectively detecting seizures from EEG data in near real-time.
IRJET-Advanced Method of Epileptic detection using EEG by Wavelet DecompositionIRJET Journal
This document proposes a method to detect epileptic seizures from EEG signals using wavelet decomposition and entropy-based feature extraction. EEG data is decomposed using wavelets and features like entropy measures and power ratios in different frequency bands are extracted. These features are then used as inputs to a k-nearest neighbors classifier to classify signals as normal, ictal or inter-ictal. The method is tested on two benchmark EEG databases and aims to increase prediction accuracy of seizure onset to help localize epileptic foci. Statistical, spectral and nonlinear features are commonly used in existing methods. The proposed method uses entropy measures like Shannon, Renyi, approximate and sample entropy along with power ratios in frequency bands as features for classification.
As rich spatiotemporal dynamics are exhibited in the human brain, it is quite complicated in nature. Sudden electrical disturbance of the brain occurs in a temporary manner and it causes epileptic seizures. Seizures may be sometimes confused with other events and sometimes it may even go unnoticed. Prediction of occurrence of an epileptic seizure is quite difficult and it is very difficult to understand the course of action. To analyze this widespread disorder of the brain, Electroencephalography (EEG) is used. It is indeed one of the best techniques to probe the activity of the brain and it is highly useful to diagnose the neurological disease. Tons of information is obtained by the EEG monitoring system and analyzing it visually is quite difficult. Therefore, the dimensionality of the EEG data is reduced with the help of dimensionality reduction techniques like Mutual Information (MI) and Matrix Factorization (MF). The values reduced through dimensionality reduction are then classified with the help of Linear Layer Networks for the classification of epilepsy from EEG Signals. Results show that when MI is used to reduce the dimensionality and classified with Linear Layer Networks an average classification accuracy of 96.60% is obtained. When MF is employed with Linear Layer Networks an average classification accuracy of 97.47% is obtained.
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
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
Ecg beat classification and feature extraction using artificial neural networ...priyanka leenakhabiya
This document discusses a proposed technique for ECG beat classification and feature extraction using artificial neural networks and discrete wavelet transform. The key steps of the proposed technique include ECG data pre-processing using discrete wavelet transform to remove noise, extracting features such as RR interval and QRS complex, designing and training an artificial neural network on the extracted features, and using an Euclidean classifier to classify different ECG cases based on the minimum distance between features. Experimental results on ECG data from the MIT-BIH database show that the proposed technique achieves high classification accuracy and sensitivity compared to previous methods.
Classification of ecg signal using artificial neural networkGaurav 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).
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).
Epileptic Seizure Detection using An EEG SensorIRJET Journal
This document presents a method for detecting epileptic seizures using an EEG sensor and signal processing techniques. It involves using an EEG headset to record raw brain wave data, filtering the signals to remove noise, applying discrete wavelet transform to extract features from different frequency bands, and using a support vector machine classifier to classify segments as normal, interictal, or ictal based on the extracted features. The proposed method aims to help doctors more accurately diagnose and monitor epilepsy in patients by objectively detecting seizures from EEG data in near real-time.
IRJET-Advanced Method of Epileptic detection using EEG by Wavelet DecompositionIRJET Journal
This document proposes a method to detect epileptic seizures from EEG signals using wavelet decomposition and entropy-based feature extraction. EEG data is decomposed using wavelets and features like entropy measures and power ratios in different frequency bands are extracted. These features are then used as inputs to a k-nearest neighbors classifier to classify signals as normal, ictal or inter-ictal. The method is tested on two benchmark EEG databases and aims to increase prediction accuracy of seizure onset to help localize epileptic foci. Statistical, spectral and nonlinear features are commonly used in existing methods. The proposed method uses entropy measures like Shannon, Renyi, approximate and sample entropy along with power ratios in frequency bands as features for classification.
As rich spatiotemporal dynamics are exhibited in the human brain, it is quite complicated in nature. Sudden electrical disturbance of the brain occurs in a temporary manner and it causes epileptic seizures. Seizures may be sometimes confused with other events and sometimes it may even go unnoticed. Prediction of occurrence of an epileptic seizure is quite difficult and it is very difficult to understand the course of action. To analyze this widespread disorder of the brain, Electroencephalography (EEG) is used. It is indeed one of the best techniques to probe the activity of the brain and it is highly useful to diagnose the neurological disease. Tons of information is obtained by the EEG monitoring system and analyzing it visually is quite difficult. Therefore, the dimensionality of the EEG data is reduced with the help of dimensionality reduction techniques like Mutual Information (MI) and Matrix Factorization (MF). The values reduced through dimensionality reduction are then classified with the help of Linear Layer Networks for the classification of epilepsy from EEG Signals. Results show that when MI is used to reduce the dimensionality and classified with Linear Layer Networks an average classification accuracy of 96.60% is obtained. When MF is employed with Linear Layer Networks an average classification accuracy of 97.47% is obtained.
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
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
This document summarizes a study on using wavelet transforms to detect and separate artifacts in EEG signals. The study aimed to minimize artifacts and noise in EEG signals without affecting the original signal. Wavelet transforms were found to be effective for analyzing non-stationary EEG signals. The results showed that wavelet transforms significantly reduced input size without compromising performance. Decomposing EEG signals using wavelet transforms extracted different frequency bands and resolved signals at different resolutions. This allowed artifacts and noise to be detected and the original signal to be recovered. Simulation results demonstrated the wavelet transform's ability to denoise EEG signals and extract key frequency components.
This document discusses using neural networks for ECG classification. It provides background on neural networks in medical applications and discusses their use in classifying arrhythmias and ischemia from ECG data. The document outlines approaches taken, including feature extraction methods and training neural network classifiers. Results show correct classification rates from 88-95% depending on the network architecture.
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
This article proposes a contribution to quantify EE
G signals outline. This technique uses two tools fo
r EEG
signal characteristics extraction. Our tests were r
ealized on the basis of 32 canals EEG canals using
Neuroscan software. EEG example demonstration is re
ferenced CZ and is sampled at 1000HZ. The
principal aim of this technique is to reduce the im
portant volume of EEG signal data Without losing an
y
information. EEG signals are quantified on the basi
s of a whole predefined levels The obtained results
show that an EEG alignment can be posted in a quant
ified form.
This study aimed to classify EEG recordings from epileptic dogs as either pre-ictal (close to a seizure) or interictal (far from a seizure) using machine learning. EEG features including coherence between brain regions and spectral entropy were extracted and used to train a linear discriminant analysis classifier. The classifier achieved AUC values ranging from 0.84 to 0.96 for classifying periods as pre-ictal or interictal in four dogs, providing supporting evidence that seizures may be predictable through EEG analysis.
This presentation discusses using machine learning algorithms like SVM and ANN to classify ECG signals as normal or abnormal based on morphological features. It extracts 12 morphological features from preprocessed ECG data in 4 databases. SVM achieved 87% accuracy on average for binary classification, comparable to related works. ANN performed best with 24 neurons in a single hidden layer, achieving 93% accuracy. While simple models were used, the morphological features proved robust across different datasets, suggesting potential for automated preliminary ECG diagnosis. Future work could optimize feature selection and apply deep learning models.
This document describes an experiment that used EEG signals to detect mental stress in human subjects. EEG signals were collected from subjects using electrodes placed according to the 10-20 international system. Stress was induced using images from the IAPS dataset. Machine learning algorithms like ICA, DWT, and PCA were used to preprocess the signals, extract features, and reduce dimensions. SVM and neural networks were then used to classify states as stressed or calm, achieving accuracies of 82% and 80% respectively. The study aimed to determine a subject's mental state as stressed or not stressed, rather than determining causes or levels of stress.
This document discusses algorithms for detecting QRS complexes in electrocardiogram (ECG) signals. It describes the wavelet transform-based algorithm developed by the authors, which involves denoising the ECG signal using wavelet coefficients and detecting QRS complexes. This algorithm is compared to existing AF2 and Pan-Tompkins algorithms, and is found to produce better results for ECG signal denoising and QRS detection. The document provides details on the wavelet transform algorithm and existing algorithms.
An Entropy-based Feature in Epileptic Seizure Prediction Algorithmiosrjce
Epilepsy prediction is a vital demand for people suffering from epileptic onset. Prediction of seizure
onsets could be very useful for drug-resistant epileptic patients. We propose an epileptic seizure prediction
algorithm to predict an onset of epilepsy and discriminate between pre-seizure periods and seizure free periods.
The proposed algorithm is based on entropy features of 60 (1 hour segmented into 60 periods) with free seizure
periods and repeated for 24 hour, and 60 (pre-seizure periods) of the CHB-MIT Scalp EEG Database (Female
less or equal 12 age). Critical values of the sample entropy and approximate entropy are estimated to locate
starting of the seizure onset. These values are taken as warning to a probably seizure starts within a specific
time. The prediction time in order of 1min- 49min is achieved in 60 seizure periods under study in this task.
SVM is used to classify pre-seizure periods from seizure free periods for the mentioned data. The performance
is evaluated and analysed
During data acquisition and transmission of biomedical signals like electrocardiography (ECG), different types of artifacts are embedded in the signal. Since an ECG is a low amplitude signal these artifacts greatly degrade the signal quality and the signal becomes noisy. The sources of artifacts are power line interference (PLI), high frequency interference electromyography (EMG) and base line wanders (BLW). Different digital filters are used in order to reduce these artifacts. ECG signal is a non-stationary signal, it is difficult to find fixed filters for the removal of interference from the ECG signal. In order to overcome these problems adaptive filters are used as they are well suited for the non-stationary environment. In this paper a new algorithm “Modified Normalized Least Mean Square” has been proposed. A comparison is made among the new algorithm and the existing algorithms like LMS, NLMS, Sign data LMS and Log LMS in terms of SNR, convergence rate and time complexity. It has been observed that the performance of new algorithm is superior to the existing ones in terms of SNR and convergence rate however it is more complex than the other algorithms. Results of simulations in MATLAB are presented and a critical analysis is made on the basis of convergence rate, signal to noise ratio (SNR), and computational time among the filtering techniques.
Classification of emotions induced by horror and relaxing movies using single-...IJECEIAES
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when k = 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K > 1 in K-NN, SVM has better average classification rate.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document discusses FPGA implementation of a wavelet-based technique for denoising EEG signals. It begins by introducing EEG signals and their clinical uses. It then describes using the wavelet transform to perform data reduction and preprocessing of five classes of EEG signals (Alpha, Beta, Gamma, Delta, Theta) generated in MATLAB. Several iterations were performed to find suitable wavelet coefficients that could correctly classify all five EEG classes. Finally, the resulting parameters were implemented on an FPGA.
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.
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
This document describes a brain-computer interface (BCI) design that uses electroencephalogram (EEG) signals from a single mental task. The method extracts spectral power from 4 brainwave bands (delta/theta, alpha, beta, gamma) across 6 electrode channels. It then uses the power and power differences as features for a neural network classifier to detect the mental task versus a resting state. Testing on 4 subjects performing 4 tasks showed classification accuracy up to 97.5% was possible when using each subject's most suitable task. The proposed BCI could potentially be used to move a cursor or select letters to allow communication.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
Wavelet-based EEG processing for computer-aided seizure detection and epileps...IJERA Editor
Many Neurological disorders are very difficult to detect. One such Neurological disorder which we are going to discuss in this paper is Epilepsy. Epilepsy means sudden change in the behavior of a human being for a short period of time. This is caused due to seizures in the brain. Many researches are going onto detect epilepsy detection through analyzing EEG. One such method of epilepsy detection is proposed in this paper. This technique employs Discrete Wave Transform (DWT) method for pre-processing, Approximate Entropy (ApEn) to extract features and Artificial Neural Network (ANN) for classification. This paper presented a detailed survey of various methods that are being used for epilepsy detection and also proposes a wavelet based epilepsy detection method
A comparative study of wavelet families for electromyography signal classific...journalBEEI
The document presents a study comparing different wavelet families for classifying electromyography (EMG) signals based on discrete wavelet transform (DWT). The proposed method involves decomposing EMG signals into sub-bands using DWT, extracting statistical features from each sub-band, and using support vector machines (SVM) for classification. Results showed that the sym14 wavelet at the 8th decomposition level achieved the best classification performance for detecting neuromuscular disorders. The study demonstrates that the proposed DWT-based approach can effectively classify EMG signals and help diagnose neuromuscular conditions.
This document presents an entropy-based algorithm for predicting epileptic seizures using EEG data. The algorithm extracts sample entropy and approximate entropy features from 1-hour periods of EEG recordings. It analyzes variations in these entropy measures in the minutes/hours before seizures to estimate prediction times ranging from 1-49 minutes. The algorithm then uses support vector machines (SVM) to classify 1-minute pre-seizure periods versus seizure-free periods based on the entropy features, in order to evaluate the seizure prediction performance. Testing uses EEG data from the CHB-MIT database involving 60 seizures from 12 female subjects aged 12 years or younger.
This document discusses a method for detecting epilepsy in EEG signals using discrete wavelet transforms and neural networks. It first preprocesses EEG data using wavelet decomposition to extract features in different frequency subbands (delta, theta, alpha, beta, gamma). Features like covariance, energy, minimum/maximum power spectral density, and entropy are then calculated from the subband signals. These features are input to a neural network for training to classify EEG patterns as epileptic or non-epileptic. The method aims to automatically detect seizures in a more time-efficient manner compared to visual inspection of long EEG recordings. It presents wavelet decomposition and feature extraction techniques, and uses neural networks for classification of epileptic vs. non-epileptic
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIERhiij
Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures
voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic
applications generally focus on the spectral content of EEG, which is the type of neural oscillations that
can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious
abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and
motivates the need for advanced signal processing techniques to aid clinicians in their interpretations.
This paper describes the application of Wavelet Transform (WT) for the processing of
Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for
feature selection and dimensionality reduction where the informative and discriminative two-dimension
features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP)
neural network. For five classification problems, the proposed model achieves a high sensitivity,
specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed
methods and those obtained with previous literature methods shows the superiority of our approach for
EEG signals classification and automated diagnosis
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
This document summarizes a study on using wavelet transforms to detect and separate artifacts in EEG signals. The study aimed to minimize artifacts and noise in EEG signals without affecting the original signal. Wavelet transforms were found to be effective for analyzing non-stationary EEG signals. The results showed that wavelet transforms significantly reduced input size without compromising performance. Decomposing EEG signals using wavelet transforms extracted different frequency bands and resolved signals at different resolutions. This allowed artifacts and noise to be detected and the original signal to be recovered. Simulation results demonstrated the wavelet transform's ability to denoise EEG signals and extract key frequency components.
This document discusses using neural networks for ECG classification. It provides background on neural networks in medical applications and discusses their use in classifying arrhythmias and ischemia from ECG data. The document outlines approaches taken, including feature extraction methods and training neural network classifiers. Results show correct classification rates from 88-95% depending on the network architecture.
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
This article proposes a contribution to quantify EE
G signals outline. This technique uses two tools fo
r EEG
signal characteristics extraction. Our tests were r
ealized on the basis of 32 canals EEG canals using
Neuroscan software. EEG example demonstration is re
ferenced CZ and is sampled at 1000HZ. The
principal aim of this technique is to reduce the im
portant volume of EEG signal data Without losing an
y
information. EEG signals are quantified on the basi
s of a whole predefined levels The obtained results
show that an EEG alignment can be posted in a quant
ified form.
This study aimed to classify EEG recordings from epileptic dogs as either pre-ictal (close to a seizure) or interictal (far from a seizure) using machine learning. EEG features including coherence between brain regions and spectral entropy were extracted and used to train a linear discriminant analysis classifier. The classifier achieved AUC values ranging from 0.84 to 0.96 for classifying periods as pre-ictal or interictal in four dogs, providing supporting evidence that seizures may be predictable through EEG analysis.
This presentation discusses using machine learning algorithms like SVM and ANN to classify ECG signals as normal or abnormal based on morphological features. It extracts 12 morphological features from preprocessed ECG data in 4 databases. SVM achieved 87% accuracy on average for binary classification, comparable to related works. ANN performed best with 24 neurons in a single hidden layer, achieving 93% accuracy. While simple models were used, the morphological features proved robust across different datasets, suggesting potential for automated preliminary ECG diagnosis. Future work could optimize feature selection and apply deep learning models.
This document describes an experiment that used EEG signals to detect mental stress in human subjects. EEG signals were collected from subjects using electrodes placed according to the 10-20 international system. Stress was induced using images from the IAPS dataset. Machine learning algorithms like ICA, DWT, and PCA were used to preprocess the signals, extract features, and reduce dimensions. SVM and neural networks were then used to classify states as stressed or calm, achieving accuracies of 82% and 80% respectively. The study aimed to determine a subject's mental state as stressed or not stressed, rather than determining causes or levels of stress.
This document discusses algorithms for detecting QRS complexes in electrocardiogram (ECG) signals. It describes the wavelet transform-based algorithm developed by the authors, which involves denoising the ECG signal using wavelet coefficients and detecting QRS complexes. This algorithm is compared to existing AF2 and Pan-Tompkins algorithms, and is found to produce better results for ECG signal denoising and QRS detection. The document provides details on the wavelet transform algorithm and existing algorithms.
An Entropy-based Feature in Epileptic Seizure Prediction Algorithmiosrjce
Epilepsy prediction is a vital demand for people suffering from epileptic onset. Prediction of seizure
onsets could be very useful for drug-resistant epileptic patients. We propose an epileptic seizure prediction
algorithm to predict an onset of epilepsy and discriminate between pre-seizure periods and seizure free periods.
The proposed algorithm is based on entropy features of 60 (1 hour segmented into 60 periods) with free seizure
periods and repeated for 24 hour, and 60 (pre-seizure periods) of the CHB-MIT Scalp EEG Database (Female
less or equal 12 age). Critical values of the sample entropy and approximate entropy are estimated to locate
starting of the seizure onset. These values are taken as warning to a probably seizure starts within a specific
time. The prediction time in order of 1min- 49min is achieved in 60 seizure periods under study in this task.
SVM is used to classify pre-seizure periods from seizure free periods for the mentioned data. The performance
is evaluated and analysed
During data acquisition and transmission of biomedical signals like electrocardiography (ECG), different types of artifacts are embedded in the signal. Since an ECG is a low amplitude signal these artifacts greatly degrade the signal quality and the signal becomes noisy. The sources of artifacts are power line interference (PLI), high frequency interference electromyography (EMG) and base line wanders (BLW). Different digital filters are used in order to reduce these artifacts. ECG signal is a non-stationary signal, it is difficult to find fixed filters for the removal of interference from the ECG signal. In order to overcome these problems adaptive filters are used as they are well suited for the non-stationary environment. In this paper a new algorithm “Modified Normalized Least Mean Square” has been proposed. A comparison is made among the new algorithm and the existing algorithms like LMS, NLMS, Sign data LMS and Log LMS in terms of SNR, convergence rate and time complexity. It has been observed that the performance of new algorithm is superior to the existing ones in terms of SNR and convergence rate however it is more complex than the other algorithms. Results of simulations in MATLAB are presented and a critical analysis is made on the basis of convergence rate, signal to noise ratio (SNR), and computational time among the filtering techniques.
Classification of emotions induced by horror and relaxing movies using single-...IJECEIAES
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when k = 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K > 1 in K-NN, SVM has better average classification rate.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document discusses FPGA implementation of a wavelet-based technique for denoising EEG signals. It begins by introducing EEG signals and their clinical uses. It then describes using the wavelet transform to perform data reduction and preprocessing of five classes of EEG signals (Alpha, Beta, Gamma, Delta, Theta) generated in MATLAB. Several iterations were performed to find suitable wavelet coefficients that could correctly classify all five EEG classes. Finally, the resulting parameters were implemented on an FPGA.
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.
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
This document describes a brain-computer interface (BCI) design that uses electroencephalogram (EEG) signals from a single mental task. The method extracts spectral power from 4 brainwave bands (delta/theta, alpha, beta, gamma) across 6 electrode channels. It then uses the power and power differences as features for a neural network classifier to detect the mental task versus a resting state. Testing on 4 subjects performing 4 tasks showed classification accuracy up to 97.5% was possible when using each subject's most suitable task. The proposed BCI could potentially be used to move a cursor or select letters to allow communication.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
Wavelet-based EEG processing for computer-aided seizure detection and epileps...IJERA Editor
Many Neurological disorders are very difficult to detect. One such Neurological disorder which we are going to discuss in this paper is Epilepsy. Epilepsy means sudden change in the behavior of a human being for a short period of time. This is caused due to seizures in the brain. Many researches are going onto detect epilepsy detection through analyzing EEG. One such method of epilepsy detection is proposed in this paper. This technique employs Discrete Wave Transform (DWT) method for pre-processing, Approximate Entropy (ApEn) to extract features and Artificial Neural Network (ANN) for classification. This paper presented a detailed survey of various methods that are being used for epilepsy detection and also proposes a wavelet based epilepsy detection method
A comparative study of wavelet families for electromyography signal classific...journalBEEI
The document presents a study comparing different wavelet families for classifying electromyography (EMG) signals based on discrete wavelet transform (DWT). The proposed method involves decomposing EMG signals into sub-bands using DWT, extracting statistical features from each sub-band, and using support vector machines (SVM) for classification. Results showed that the sym14 wavelet at the 8th decomposition level achieved the best classification performance for detecting neuromuscular disorders. The study demonstrates that the proposed DWT-based approach can effectively classify EMG signals and help diagnose neuromuscular conditions.
This document presents an entropy-based algorithm for predicting epileptic seizures using EEG data. The algorithm extracts sample entropy and approximate entropy features from 1-hour periods of EEG recordings. It analyzes variations in these entropy measures in the minutes/hours before seizures to estimate prediction times ranging from 1-49 minutes. The algorithm then uses support vector machines (SVM) to classify 1-minute pre-seizure periods versus seizure-free periods based on the entropy features, in order to evaluate the seizure prediction performance. Testing uses EEG data from the CHB-MIT database involving 60 seizures from 12 female subjects aged 12 years or younger.
This document discusses a method for detecting epilepsy in EEG signals using discrete wavelet transforms and neural networks. It first preprocesses EEG data using wavelet decomposition to extract features in different frequency subbands (delta, theta, alpha, beta, gamma). Features like covariance, energy, minimum/maximum power spectral density, and entropy are then calculated from the subband signals. These features are input to a neural network for training to classify EEG patterns as epileptic or non-epileptic. The method aims to automatically detect seizures in a more time-efficient manner compared to visual inspection of long EEG recordings. It presents wavelet decomposition and feature extraction techniques, and uses neural networks for classification of epileptic vs. non-epileptic
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIERhiij
Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures
voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic
applications generally focus on the spectral content of EEG, which is the type of neural oscillations that
can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious
abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and
motivates the need for advanced signal processing techniques to aid clinicians in their interpretations.
This paper describes the application of Wavelet Transform (WT) for the processing of
Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for
feature selection and dimensionality reduction where the informative and discriminative two-dimension
features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP)
neural network. For five classification problems, the proposed model achieves a high sensitivity,
specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed
methods and those obtained with previous literature methods shows the superiority of our approach for
EEG signals classification and automated diagnosis
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
mHealth applications have shown promise in supporting the delivery of health services in peoples’ daily life. Recently, the Ministry of Health in the Kingdom of Saudi Arabia (MOH) has launched several mHealth applications to develop work mechanisms. Our study aimed to identify and understand the design of mHealth apps by classifying their persuasive features using the Persuasive Systems Design (PSD) model and expert evaluation method. This paper presents the distinct persuasive features applied in recent applications launched by MOH for public users called “Sehha & Mawid” Apps. The results revealed the extensive use of persuasive features; particularly features related to credibility support, dialogue support and primary task support respectively. The implementation and design of social support features were found to be poor; this could be due to the nature of the apps or lack of knowledge from the developers’
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
Denoising Techniques for EEG Signals: A ReviewIRJET Journal
The document reviews techniques for denoising EEG signals contaminated with artifacts. It discusses regression, blind source separation (BSS) including principal component analysis (PCA) and independent component analysis (ICA), wavelet transform, and empirical mode decomposition (EMD). Each method has benefits and limitations. Combining multiple current approaches can address individual constraints and provide superior outcomes than single algorithms alone by overcoming each other's limitations.
EEG Signal Classification using Deep Neural NetworkIRJET Journal
This document discusses using deep neural networks to classify EEG signals. It proposes using a convolutional neural network to analyze recurrence plots generated from EEG data in order to distinguish between focal and non-focal EEG signals. The recurrence plots are generated from EEG data collected from epilepsy patients. The convolutional neural network is then used to classify the recurrence plots to identify patterns associated with focal versus non-focal EEG signals. This classification of EEG signals could help doctors locate epileptic foci to inform treatment decisions.
Different Approaches for the Detection of Epilepsy and Schizophrenia Using EE...IRJET Journal
This document presents two approaches for detecting epilepsy and schizophrenia using EEG signal analysis.
The first approach uses Welch power spectral density analysis and machine learning classifiers to distinguish healthy individuals from those with epilepsy, achieving up to 99.16% accuracy. The second approach uses a deep learning model called ChronoNet on a different dataset, achieving 97.20% accuracy in detecting epilepsy.
A second comparison analyzes two methods for distinguishing healthy and schizophrenic patients. The first extracts frequency domain features and uses classifiers, achieving 85.11% accuracy. The second uses a convolutional neural network, outperforming a previous work by 4%.
In both comparisons, the deep learning approaches achieved higher accuracy than traditional machine learning methods in
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.
The document describes a thesis submitted for the degree of Bachelor of Technology in Electrical Engineering. The thesis aims to classify electrocardiogram (ECG) waveforms in real-time to diagnose cardiac diseases. It uses the discrete Daubechies wavelet transform to preprocess ECG signals and extract features. These features are then classified using a multilayer perceptron neural network. The classification model was implemented in SIMULINK software to simulate real-time detection and verify its performance. The thesis discusses ECG basics, wavelet transforms, neural networks, and presents results of signal decomposition, network training, and SIMULINK implementation.
Eeg time series data analysis in focal cerebral ischemic rat modelijbesjournal
The mammalian brain exists in a number of attractors. In order to characterize these attractors we have collected the time series data from the EEG recording of rat models. The time series was obtained by recording of the frontoparietal, occipital and temporal regions of the rat brain. Significant changes have
been observed in the dimensionalities of these brain attractors between the normal state, focal ischemic
state and the drug induced state. Thus, these three states were characterized by unique lyapunov exponents,
correlation dimensions and embedding dimensions. The inverse of the lyapunov exponent gave us the long
term coherence of the rat brain and was found to differ for the three states. The autocorrelation function
measured the mean similarity of the EEG signal with itself after a time t. The degree of decay was high indicating that there was maximum correlation in the time series. Thus, the autocorrelation functions clearly indicate the effect of focal cerebral ischemia and drugs induced on the rat brain.
This document summarizes and compares algorithms for detecting and predicting epileptic seizures from electroencephalogram (EEG) signals. It begins by introducing the challenges of epilepsy and importance of automatic seizure detection and prediction. It then provides an overview of state-of-the-art algorithms operating in different transform domains, including time, frequency, wavelet, empirical mode decomposition, singular value decomposition, and principal/independent component analysis domains. The document concludes by comparing seizure detection and prediction methods and discussing future research directions.
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.
Approximate entropy (ApEn) is a technique used to quantify the unpredictability and regularity of fluctuations in time-series data. It reflects the likelihood that similar patterns will not be followed by additional similar observations. ApEn is useful for relatively short, noisy time-series data, as it is less affected by noise and has lower computational demands than other complexity measures. ApEn has been used to successfully distinguish patient groups in applications like endocrine hormone secretion and epilepsy detection from EEG data, with accuracies over 90% in some cases. It has advantages over entropy as it can be used on smaller sample sizes and applied in real-time applications.
New Method of R-Wave Detection by Continuous Wavelet TransformCSCJournals
In this paper we have employed a new method of R-peaks detection in electrocardiogram (ECG) signals. This method is based on the application of the discretised Continuous Wavelet Transform (CWT) used for the Bionic Wavelet Transform (BWT). The mother wavelet associated to this transform is the Morlet wavelet. For evaluating the proposed method, we have compared it to others methods that are based on Discrete Wavelet Transform (DWT). In this evaluation, the used ECG signals are taken from MIT-BIH database. The obtained results show that the proposed method outperforms some conventional techniques used in our evaluation.
Health electroencephalogram epileptic classification based on Hilbert probabi...IJECEIAES
This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status.
Detection and Removal of Non-Responsive Channels and Trials in Evoked Potenti...sipij
The primary goal of this research work is to detect and remove non responsive channels and trials in evoked potentials by tracing out the signals with very low energy. This is done by calculating the energy of the average evoked potential of each channel, and the energy of the average evoked potential of each trial. Then channel wise and trial wise median test is conducted to detect and remove non-responsive channels and trials. An attempt has been made to apply these techniques to 14-channel visual evoked potentials (VEPs) obtained from four different subjects.
Effective electroencephalogram based epileptic seizure detection using suppo...IJECEIAES
Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.
Analysis of Various Signals Acquired from Uterine Contraction to Determine Tr...IRJET Journal
This document discusses analyzing uterine contraction signals to determine true and false labor. EHG signals are acquired from the uterine muscle through electrodes and preprocessed. Linear (mean, median) and nonlinear (entropy) features are extracted from the EHG signals. A support vector machine classifier is applied to the features to classify recordings as term or preterm labor based on the features. The SVM classifier achieved good performance in classifying the two stages of labor based on the extracted EHG signal features.
Advanced Support Vector Machine for classification in Neural NetworkAshwani Jha
This paper proposes a method to classify EEG signals using a support vector machine (SVM) classifier combined with a quicksort sorting algorithm (SVM-Q). The method involves extracting phase locking value (PLV) features from EEG data, sorting the feature vectors using quicksort, and then applying SVM classification. When tested on two BCI competition datasets, SVM-Q achieved classification accuracies of 86% and 77%, outperforming plain SVM which achieved 62% and 54% respectively. The paper concludes that adding a sorting algorithm to SVM can improve its classification performance for brain-computer interface applications.
Detection of Type-B Artifacts in VEPs using Median Deviation AlgorithmIOSRJECE
The primary goal of this research work is to introduce temporal artifact detection strategy to detect non responsive channels and trials in visual evoked potentials(VEPs) by tracing out the signals with very low energy and to remove artifacts in multichannel visual evoked potentials. The non responsive channels and trials are identified by calculating the energy of the average evoked potential of each channel, and the energy of the average evoked potential of each trial. Then channel wise and trial wise median test is conducted to detect and remove non-responsive channels and trials. An artifact is defined as any signal that may lead to inaccurate classifier parameter estimation. Temporal domain artifact detection tests include: a clipping (CL) test detect amplitude clipped EPs in each channel, a standard deviation (STD) test that can detect signals with little or abnormal variations in each channel, a kurtosis (KU) test to detect unusual signals that are not identified by STD and CL tests and median deviation test to detect signals containing large number of samples with very small deviation from their normal values. An attempt has been made to apply these techniques to 14-channel visual evoked potentials (VEPs) obtained from different subjects.
Similar to IRJET- Analysis of Epilepsy using Approximate Entropy Algorithm (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
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.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
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
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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.