Abstract
The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy.
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Bouaziz, B., Chaari, L., Batatia, H., Quintero-Rincón, A. (2019). Epileptic Seizure Detection Using a Convolutional Neural Network. In: Chaari, L. (eds) Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Advances in Predictive, Preventive and Personalised Medicine, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-11800-6_9
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DOI: https://doi.org/10.1007/978-3-030-11800-6_9
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