SeizureNet: a model for robust detection of epileptic seizures based on convolutional neural network. Issue 3 (7th September 2020)

• Record Type: Journal Article
• Authors: Zhao, Wei; Wang, Wenfeng
• Is Part Of: Cognitive computation and systems. Volume 2:Issue 3(2020)
• Page Start: 119
• Page End: 124
• Publication Date: 2020-09-07
• Subjects: neurophysiology -- medical signal processing -- signal classification -- electroencephalography -- medical signal detection -- convolutional neural nets -- medical disorders -- feature extraction; robust detection -- epileptic seizures -- convolutional neural network -- neurological disorder -- electroencephalogram signals -- extensive automatic detection algorithms -- EEG classification problems -- time‐invariant feature extraction -- single‐channel EEG signals -- high‐level features -- nonseizure classification -- SeizureNet -- softmax layer -- benchmark database -- ten‐fold cross‐validation approach; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs.2020.0011
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract : Epilepsy is a neurological disorder and generally detected by electroencephalogram (EEG) signals. The manual inspection of epileptic seizures is a time‐consuming and laborious process. Extensive automatic detection algorithms were proposed by using traditional approaches, which show good accuracy for several specific EEG classification problems but perform poorly in others. To address this issue, the authors present a novel model, named SeizureNet, for robust detection of epileptic seizures using EEG signals based on convolutional neural network. Firstly, they utilise two convolutional neural networks to extract time‐invariant features from single‐channel EEG signals. Then, a fully connected layer is employed to learn high‐level features. Finally, these features are supplied to a softmax layer to classify. They evaluated the model on a benchmark database provided by the University of Bonn and adopted a ten‐fold cross‐validation approach. The proposed model has achieved the accuracy of 98.50–100.00% in classifying non‐seizure and seizure, 97.00–99.00% in classifying healthy, inter‐ictal and ictal, and 95.84% in classifying among five‐class EEG states.