Electroencephalogram spike detection and classification by diagnosis with convolutional neural network

Abstract

This work presents convolutional neural network (CNN) based methodology for electroencephalogram (EEG) classification by diagnosis: benign childhood epilepsy with centrotemporal spikes (rolandic epilepsy) (Group I) and structural focal epilepsy (Group II). Manual classification of these groups is sometimes difficult, especially, when no clinical record is available, thus presenting a need for an algorithm for automatic classification. The presented algorithm has the following steps: (i) EEG spike detection by morphological filter based algorithm; (ii) classification of EEG spikes using preprocessed EEG signal data from all channels in the vicinity of the spike detected; (iii) majority rule classifier application to all EEG spikes from a single patient. Classification based on majority rule allows us to achieve 80% average accuracy (despite the fact that from a single spike one would obtain only 58% accuracy).

Keywords

• electroencephalogram
• convolutional neural network
• machine learning
• classification
• epilepsy