Detection of epileptic seizure events using pre‐trained convolutional neural network, VGGNet and ResNet

Abstract

AbstractEpilepsy is a life threatening neurological disorder. The person with epilepsy suffers from recurrent seizures. Sudden emission of electrical signal in the nerves of the human brain is called seizure event. The most widely used method for diagnosing epilepsy is analysing electroencephalogram signals in short called as EEG signals collected from the scalp of the patient. The EEG data are normally used for seizure detection. If the recurrent seizure signals are detected in the input EEG dataset, then it can be considered as the presence of epilepsy disorder. Manual inspection of seizure signals in the EEG data is a laborious process. An automated system is very crucial for the neurologists to identify seizures. In this paper, an automated seizure detection method is presented using deep learning method, pre‐trained convolutional neural network architecture. Freely available EEG dataset from Temple University Hospital database is used for the study. The pre‐trained CNN networks, VGGNet and ResNet are used for classifying the seizure activities from non‐seizure activities. CNNs are extremely good in learning the features of the input data. A very large dataset from TUH is provided as input to the multiple layers of CNN model. The same data is fed to VGGNet and ResNet models. The results of CNN, VGGNet and ResNet models are assessed using performance metrics accuracy, AUC, precision and recall. All the three models gave extremely good performance compared to state‐of‐the‐art works in the literature. In comparison VGGNet performed with little higher results giving 97% accuracy, 96% AUC, 97% precision and 79% recall.