Automatic absence seizure detection and early detection system using CRNN-SVM Online publication date: Fri, 08-Nov-2019
by Niha Kamal Basha; Aisha Banu Wahab
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 11, No. 4, 2019
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).
Online publication date: Fri, 08-Nov-2019
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reasoning-based Intelligent Systems (IJRIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com