scholarly journals Analysis of EEG signal of specific epileptic patient prior to its occurrence

SCITECH Nepal ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 16-23
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Amit Shah ◽  
Bhoj Raj Thapa
Keyword(s):  
Epileptic Seizure ◽  
Predictive Value ◽  
Epileptic Patient ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unique Pattern ◽  
Multi Resolution Analysis ◽  
The Individual

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper is focussed on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi-resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MJT­-BIH of one of the patient with 41 different cases is used. The result showed that a unique pattern is observed during the spectral analysis of the signal over different bands with positive predictive value of 100%, negative predictive value of 82.35% and the overall accuracy of 85.37%. This unique pattern, basically energy burst in two of the bands of the signal can be used as important feature for the early detection of the epileptic seizure. All the results have been simulated within the Matlab environment.

scholarly journals Implementing Neural Network and Multi resolution analysis in EEG signal for early detection of epilepsy

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Bhojraj Thapa
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Epileptic Seizure ◽  
Principal Component ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper focuses on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MIT-BIH of seven patients with different cases of epileptic seizure was used. The result with one of the patients showed presence of a unique pattern during the spectral analysis of the signal over different bands. Hence, based on the first patient, similar region is selected with the other patients and the multi-resolution analysis along with the principal component analysis (PCA) for the dimension reduction is carried out. Finally, these are treated with neural network to perform the classification of the signal either the epilepsy is occurring or not. The final results showed 100% accuracy with the detection with the neural network however it uses a large amount of data for analysis. Thus, the same was tested with dimension reduction using PCA which reduced the average accuracy to 89.51%. All the results have been simulated within the Matlab environment.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features

2018 ◽  
Vol 28 (07) ◽  
pp. 1850003 ◽  
Author(s):  
Yang Li ◽  
Weigang Cui ◽  
Meilin Luo ◽  
Ke Li ◽  
Lina Wang
Keyword(s):  
Epileptic Seizure ◽  
Classification Accuracy ◽  
Epileptic Seizures ◽  
Gaussian Mixture ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Band Frequency ◽  
Time Frequency ◽  
Frequency Components ◽  
Occurrence Matrix

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

scholarly journals Automated Epileptic Seizures Detection and Classification

2019 ◽  
pp. 555-560 ◽  
Author(s):  
Harshavarthini S ◽  
Aswathy M. P. ◽  
Harshini P ◽  
Priyanka G
Keyword(s):  
Epileptic Seizure ◽  
Probabilistic Neural Network ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Features Selection ◽  
Eeg Signals ◽  
Human Errors ◽  
Visual Identification ◽  
Time Period ◽  
Long Time

Detection of epileptic seizure activities from multi-channel electroencephalogram (EEG) signals plays a giant position inside the timely treatment of the sufferers with epilepsy. Visual identification of epileptic seizure in long-time period EEG is bulky and tedious for neurologists, which may additionally cause human errors. An automated device for accurate detection of seizures in a protracted-time period multi-channel EEG is crucial for the scientific prognosis. The features selection is based on discrete wavelet transformation (DWT).and feature extraction based GLCM. In the last stage, Probabilistic Neural Network is employed to classify the Normal and epileptic EEG signals.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ahmed I. Sharaf ◽  
Mohamed Abu El-Soud ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Feature Selection ◽  
Correlation Energy ◽  
Epileptic Seizures ◽  
Compact Set ◽  
Eeg Signal ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Eeg Signals ◽  
Computer Aided ◽  
Firefly Optimization

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew’s correlation coefficient.

scholarly journals Analysis of Seizure EEG in Kindled Epileptic Rats

2007 ◽  
Vol 8 (4) ◽  
pp. 225-234 ◽  
Author(s):  
A. K. Sen ◽  
M. J. Kubek ◽  
H. E. Shannon
Keyword(s):  
Epileptic Seizures ◽  
Wavelet Spectrum ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Frequency Changes ◽  
Global Wavelet Spectrum ◽  
Frequency Variations ◽  
Dominant Frequencies

Using wavelet analysis we have detected the presence of chirps in seizure EEG signals recorded from kindled epileptic rats. Seizures were induced by electrical stimulation of the amygdala and the EEG signals recorded from the amygdala were analyzed using a continuous wavelet transform. A time–frequency representation of the wavelet power spectrum revealed that during seizure the EEG signal is characterized by a chirp-like waveform whose frequency changes with time from the onset of seizure to its completion. Similar chirp-like time–frequency profiles have been observed in newborn and adult patients undergoing epileptic seizures. The global wavelet spectrum depicting the variation of power with frequency showed two dominant frequencies with the largest amounts of power during seizure. Our results indicate that a kindling paradigm in rats can be used as an animal model of human temporal lobe epilepsy to detect seizures by identifying chirp-like time–frequency variations in the EEG signal.

EEG Signals Classification Using Support Vector Machine

2020 ◽  
Vol 12 (2) ◽  
pp. 215-224
Author(s):  
Abdelhakim Ridouh ◽  
Daoud Boutana ◽  
Salah Bourennane
Keyword(s):  
Support Vector Machine ◽  
Wavelet Decomposition ◽  
Moving Average ◽  
Real Life ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Decomposition Level ◽  
Sensitivity Specificity

We address with this paper some real-life healthy and epileptic EEG signals classification. Our proposed method is based on the use of the discrete wavelet transform (DWT) and Support Vector Machine (SVM). For each EEG signal, five wavelet decomposition level is applied which allow obtaining five spectral sub-bands correspond to five rhythms (Delta, Theta, Alpha, Beta and gamma). After the extraction of some features on each sub-band (energy, standard deviation, and entropy) a moving average (MA) is applied to the resulting features vectors and then used as inputs to SVM to train and test. We test the method on EEG signals during two datasets: normal and epileptics, without and with using MA to compare results. Three parameters are evaluated such as sensitivity, specificity, and accuracy to test the performances of the used methods.

scholarly journals Wearable Detection Systems for Epileptic Seizure: A review

2020 ◽  
Vol 16 (2) ◽  
pp. 1-13
Author(s):  
Alla Fikrat Alwindawi ◽  
Osman Nuri UÇAN ◽  
Ameer Hussein Morad
Keyword(s):  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Literature Survey ◽  
Research Articles ◽  
Low Power Consumption ◽  
Eeg Signal ◽  
Detection Systems ◽  
Multi Body ◽  
Video Eeg

The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head. Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal. The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple body signals, new algorithm methods, and detection devices that are commercially available. As a result, the reviewing process shows that there are many research articles that have covered wearable seizure detection systems that based on body signals. The more effective monitoring and detection seizure system is the system that uses multi-body signals, is highly comfortable and has low power consumption.

scholarly journals A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Gaowei Xu ◽  
Tianhe Ren ◽  
Yu Chen ◽  
Wenliang Che
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Epileptic Seizure ◽  
Signal Analysis ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Eeg Signal ◽  
Learning Methods ◽  
One Dimensional ◽  
Eeg Signal Analysis

Frequent epileptic seizures cause damage to the human brain, resulting in memory impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures and provide medical treatment in a timely manner. Currently, medical experts recognize epileptic seizure activity through the visual inspection of electroencephalographic (EEG) signal recordings of patients based on their experience, which takes much time and effort. In view of this, this paper proposes a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) model for automatic recognition of epileptic seizures through EEG signal analysis. Firstly, the raw EEG signal data are pre-processed and normalized. Then, a 1D convolutional neural network (CNN) is designed to effectively extract the features of the normalized EEG sequence data. In addition, the extracted features are then processed by the LSTM layers in order to further extract the temporal features. After that, the output features are fed into several fully connected layers for final epileptic seizure recognition. The performance of the proposed 1D CNN-LSTM model is verified on the public UCI epileptic seizure recognition data set. Experiments results show that the proposed method achieves high recognition accuracies of 99.39% and 82.00% on the binary and five-class epileptic seizure recognition tasks, respectively. Comparing results with traditional machine learning methods including k-nearest neighbors, support vector machines, and decision trees, other deep learning methods including standard deep neural network and CNN further verify the superiority of the proposed method.

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