Automated Seizure Detection through EEG Analysis and Deep Learning Technique - Srinivas Nowduri; M. Madhusudhana Subramanyam (Journal of Artificial Intelligence and System Modelling)
Detecting epileptic seizures automatically through intelligent methods has been a main challenge in recent years. This is because neurologists are burdened with analyzing electroencephalogram (EEG) data via visual inspection, and automating the process can reduce their workload. However, one of the challenges of automatic seizure detection using EEG analysis is extracting optimal features that can distinguish between different states of epilepsy. To address this issue, this research proposes a new approach for automatically identifying epileptic seizures using a deep convolutional network. The network has nine convolutional layers and one fully-connected layer, which learn the features hierarchically and identify epileptic seizures through the EEG analysis. The designed deep network was applied to the epileptic EEG dataset from the University of Bonn. The results showed that 100% accuracy, 100% sensitivity, and 100% specificity were achieved using the proposed method and 10-fold cross-validation for classifying the three investigated EEG conditions (i.e., normal, preictal, and ictal states). The proposed architecture was very efficient in classifying epileptic EEG data. Due to the high accuracy of the algorithm, it can be used for automatic detection of different stages of epilepsy using large EEG data.