High performance EEG feature extraction for fast epileptic seizure detection

2017 ◽  
Author(s):  
Ramy Hussein ◽  
Mohamed Elgendi ◽  
Rabab Ward ◽  
Amr Mohamed
Keyword(s):  
Feature Extraction ◽  
Epileptic Seizure ◽  
High Performance ◽  
Seizure Detection ◽  
Epileptic Seizure Detection

Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

2020 ◽  
Vol 65 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Chahira Mahjoub ◽  
Régine Le Bouquin Jeannès ◽  
Tarek Lajnef ◽  
Abdennaceur Kachouri
Keyword(s):  
Epileptic Seizure ◽  
High Performance ◽  
Visual Analysis ◽  
Binary Classification ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Epileptic Seizure Detection

AbstractElectroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.

Feature extraction from EEG spectrograms for epileptic seizure detection

2020 ◽  
Vol 133 ◽  
pp. 202-209 ◽  
Author(s):  
Ricardo Ramos-Aguilar ◽  
J. Arturo Olvera-López ◽  
Ivan Olmos-Pineda ◽  
Susana Sánchez-Urrieta
Keyword(s):  
Feature Extraction ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Seizure Detection

scholarly journals A review of feature extraction and performance evaluation in epileptic seizure detection using EEG

2020 ◽  
Vol 57 ◽  
pp. 101702 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Krisnachai Chomtho ◽  
Jitkomut Songsiri
Keyword(s):  
Feature Extraction ◽  
Performance Evaluation ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Seizure Detection ◽  
And Performance

A SEIZURE DETECTION METHOD BASED ON WELL-SOLVED NONLINEAR AND NON-STATIONARY PROBLEMS WITH ELECTROENCEPHALOGRAPHIC SIGNALS

2018 ◽  
Vol 30 (05) ◽  
pp. 1850037
Author(s):  
Xia Zhang ◽  
Haijun Chen
Keyword(s):  
Feature Extraction ◽  
Epileptic Seizure ◽  
Classification Accuracy ◽  
Seizure Detection ◽  
Extraction Methods ◽  
Epileptic Seizure Detection ◽  
Healthy State ◽  
Cepstral Coefficients ◽  
The Moment ◽  
Stationary Problems

The main focus of this paper is to solve the nonlinear and non-stationary problems in electroencephalographic (EEG) signals, which has been solved by the proposed method by using convolutional neural networks (CNN) as the classifiers and assembling Local Mean Decomposition (LMD) and cepstral coefficients as the feature extraction methods to achieve epileptic seizure detection with signal analysis and processing. In this proposed method, LMD and cepstral coefficients have been employed to solve the nonlinear and non-stationary problems in feature extraction and infusion, and then, the feature can be employed to feed to the recognition engine named CNN, and finally, the epileptic seizure detection can be achieved by this step. Publicly available EEG database from the University of Bonn (UoB), Germany had been used to verify the effectiveness and robustness of this proposed method on feature extraction. The complete dataset of total 7960 EEG segments, three recognition problems marked as AB versus CD versus E, the average classification accuracy of these segments can be generally obtained as highly as 99.84%, the maximal classification accuracy is 99.87%, and the lowest recognition accuracy is 98.74%. To the best of our knowledge, the excellent performance of the proposed method has shown that this method can be employed to track the patient’s healthy state and monitor the moment of epilepsy seizure.

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