Semi-Supervised EEG Signals Classification System for Epileptic Seizure Detection

In the past few decades, measuring and recording the brain electrical activities using Electroencephalogram (EEG) has become a standout amongst the tools utilized for neurological disorders' diagnosis, especially seizure detection. In this letter, a novel epileptic seizure detection system base...

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Bibliographic Details
Published in:IEEE signal processing letters 2019-12, Vol.26 (12), p.1922-1926
Main Authors: Abdelhameed, Ahmed M., Bayoumi, Magdy
Format: Article
Language:English
Subjects:
Classification
Convulsions & seizures
cross-validation
Deep learning
Electroencephalography
Epilepsy
epileptic seizure detection
Feature extraction
Machine learning
Neurological diseases
Seizures
Signal classification
Training
variational autoencoder
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Summary:In the past few decades, measuring and recording the brain electrical activities using Electroencephalogram (EEG) has become a standout amongst the tools utilized for neurological disorders' diagnosis, especially seizure detection. In this letter, a novel epileptic seizure detection system based on classifying raw EEG signals' recordings, eliminating the overhead of engineered feature extraction, is proposed. The system employs a mixing of unsupervised and supervised deep learning utilizing a one-dimensional convolutional variational autoencoder. To ascertain the robustness of the system against classifying unseen data, the evaluation of the proposed system is done using k-fold cross-validation. The classification results between normal and ictal cases have achieved a 100% accuracy while the classification results between the normal, inter-ictal and ictal cases accomplished a 99% overall accuracy which makes our system one of the most efficient among other state-of-the-art systems.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2019.2953870