Single Channel EEG Signal for Automatic Detection of Absence Seizure Using Convolutional Neural Network

Background: In this paper, a Convolutional Neural Network to extract the seizure features and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalography. Objective: To perform automatic detection of absence seizure using single channel electroencephalography signal as input. Methods: This data is then used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1) convolutional layer – which extract the features in the form of vector 2) Pooling layer – the dimensionality of output from convolutional layer is reduced and 3) Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. Results: The paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. Conclusion: The proposed approach outperforms the performance of Support Vector Machine in automatic detection of absence seizure.