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06.04.2022

Predicting Epileptic Seizures from EEG Spectral Band Features Using Convolutional Neural Network

verfasst von: Kuldeep Singh, Jyoteesh Malhotra

Erschienen in: Wireless Personal Communications | Ausgabe 3/2022

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Abstract

Epilepsy, a globally growing chronic nervous disorder, affects the lives of millions of patients annually through the abrupt occurrence of recurrent seizures. It could result in serious injuries or the death of patients in various accidents. Thus, the automatic prediction of epileptic seizures is essential for alerting the patients well before its actual onset, thereby increasing their chances of being safe. In the present times, internet of things assisted technologies have started exploring the potential of cloud as well as fog computing services for providing solutions to such nervous disorders using deep learning. The present paper also proposes a convolutional neural network-based automatic seizure prediction model in a cloud-fog integrated scenario. This model utilizes EEG segments of shorter time durations, which are characterized by discrete spectral features, such as spectral power and mean amplitude spectrum. These features are extracted from five spectral sub-bands of 23-channel EEG signal recordings, including delta, theta, alpha, beta and gamma sub-bands. The performance evaluation through various simulations reveals the efficiency of the proposed model for seizure prediction using EEG segment duration of 30 s. In conclusion, the analysis of simulation results, as well as performance comparison with other contemporary methods evidently disclose that the proposed EEG spectral band features based convolutional neural network approach is a competent method for accurate epileptic seizure prediction in real-time with an average accuracy of 97.4%, average sensitivity of 98%, average specificity of 96.6% and average false discovery rate of 2.7% only.

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Titel: Predicting Epileptic Seizures from EEG Spectral Band Features Using Convolutional Neural Network
verfasst von: Kuldeep Singh
Jyoteesh Malhotra
Publikationsdatum: 06.04.2022
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 3/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-09678-y

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