Authors: Niha Kamal Basha; Aisha Banu Wahab
Addresses: Department of Computer Science and Engineering, BSA Crescent Institute of Science and Technology, 6000048, Vandalur, Chennai, India ' Department of Computer Science and Engineering, BSA Crescent Institute of Science and Technology, 6000048, Vandalur, Chennai, India
Abstract: In this paper the new model is proposed to automatically detect and predict absence seizure using hybrid deep learning algorithm [convolutional recurrent neural network (CRNN)] with single channel electroencephalography (EEG) only as input. This model comprises of four steps: 1) single channel segmentation process; 2) extraction of relevant features using convolution network; 3) recurrent network for detection and early detection; 4) SVM have been used as last layer to obtain a result with respect to time. This model enhances the feature extraction by feeding the raw input into convolutional layer, improves the detection with gated recurrent unit (GRU) and reduces the early detection rate with support vector machine (SVM). Our proposed model achieves 100% overall accuracy on seizure detection as normal and absence seizure and detect within three seconds of the overall seizure duration. Also this model can be act as a generic model for classification task with detection and early detection of bio-signal (EEG, ECG and EMG).
Keywords: absence seizure; convolutional recurrent neural network; RNN; electroencephalography; gated recurrent unit; GRU; normal and ictal subject; rhythmic frequency; seizure detection; early detection; sampling rate; support vector machine; SVM; statistical features.
International Journal of Reasoning-based Intelligent Systems, 2019 Vol.11 No.4, pp.330 - 335
Received: 27 Jul 2018
Accepted: 04 Nov 2018
Published online: 05 Nov 2019 *