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<p>Epileptic seizure attack is caused by abnormal brain activity of human subjects. Certain cases will lead to death. The detection and diagnosis is therefore an important task. It can be performed either by direct patient activity during seizure or by electroencephalogram (EEG) signal analysis by neurologists. EEG signal processing and detection of seizures using machine learning techniques make this task easier than manual detection. To overcome this problem related to a neurological disorder, we have proposed the ensemble learning technique for improved detection of epilepsy seizures from EEG signals. In the first stage, EEG signal decomposition is done by utilizing empirical wavelet transform (EWT) for smooth analysis in terms of sub-bands. Further, features are extracted from each sub. Time and frequency domain features are the two categories used to extract the statistical features. These features are used in a stacked ensemble of deep neural network (DNN) model along with multilayer Perceptron (MLP) for the detection and classification of ictal, inter-ictal, and pre-ictal (normal) signals. The proposed method is verified using two publicly available datasets provided by the University of Bonn (UoB dataset) and Neurology and Sleep Center - New Delhi (NSC-ND dataset). The proposed algorithm resulted in 98.93 % and 98 % accuracy for the UoB and NSC-ND datasets, respectively.</p>
</abstract>

Epileptic seizure attack is caused by abnormal brain activity of human subjects. Certain cases will lead to death. The detection and diagnosis is therefore an important task. It can be performed either by direct patient activity during seizure or by electroencephalogram (EEG) signal analysis by neurologists. EEG signal processing and detection of seizures using machine learning techniques make this task easier than manual detection. To overcome this problem related to a neurological disorder, we have proposed the ensemble learning technique for improved detection of epilepsy seizures from EEG signals. In the first stage, EEG signal decomposition is done by utilizing empirical wavelet transform (EWT) for smooth analysis in terms of sub-bands. Further, features are extracted from each sub. Time and frequency domain features are the two categories used to extract the statistical features. These features are used in a stacked ensemble of deep neural network (DNN) model along with multilayer Perceptron (MLP) for the detection and classification of ictal, inter-ictal, and pre-ictal (normal) signals. The proposed method is verified using two publicly available datasets provided by the University of Bonn (UoB dataset) and Neurology and Sleep Center - New Delhi (NSC-ND dataset). The proposed algorithm resulted in 98.93 % and 98 % accuracy for the UoB and NSC-ND datasets, respectively.

Epileptic Seizure Detection using Deep Ensemble Network with Empirical Wavelet Transform

Centurion University of Technology and Management

ITER, Siksha ‘O’ Anusandhan (Deemed to be University)

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Epileptic seizure attack is caused by abnormal brain activity of human subjects. Certain cases will lead to death. The detection and diagnosis is therefore an...

Epileptic Seizure Detection using Deep Ensemble Network with Empirical Wavelet Transform

Pubblicato online: 13 ago 2021

Pagine: 110 - 116

Ricevuto: 24 apr 2021

Accettato: 05 ago 2021

DOI: https://doi.org/10.2478/msr-2021-0016

Parole chiave
Epilepsy, Empirical Wavelet Transform, DNN, Ensemble, EEG Classification

© 2021 Sreelekha Panda et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Epileptic Seizure Detection using Deep Ensemble Network with Empirical Wavelet Transform

Pubblicato online: 13 ago 2021

Pagine: 110 - 116

Ricevuto: 24 apr 2021

Accettato: 05 ago 2021

DOI: https://doi.org/10.2478/msr-2021-0016

Parole chiaveEpilepsy, Empirical Wavelet Transform, DNN, Ensemble, EEG Classification

© 2021 Sreelekha Panda et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Epilepsy, Empirical Wavelet Transform, DNN, Ensemble, EEG Classification