MATEC Web Conf.
Volume 210, 201822nd International Conference on Circuits, Systems, Communications and Computers (CSCC 2018)
|Number of page(s)||8|
|Published online||05 October 2018|
Epileptic Seizure Prediction over EEG Data using Hybrid CNN-SVM Model with Edge Computing Services
LNM Institute of Information Technology, Jaipur, India
2 National Ilan University, Yilan County, Taiwan
* Corresponding author: firstname.lastname@example.org
Epilepsy is one of the most common neurological disorders, which is characterized by unpredictable brain seizure. About 30% of the patients are not even aware that they have epilepsy and many have to undergo surgeries to relieve the pain. Therefore, developing a robust brain-computer interface for seizure prediction can help epileptic patients significantly. In this paper, we propose a hybrid CNN-SVM model for better epileptic seizure prediction. A convolutional neural network (CNN) consists of a multilayer structure, which can be adapted and modified according to the requirement of different applications. A support vector machine is a discriminative classifier which can be described by a separating optimal hyperplane used for categorizing new samples. The combination of CNN and SVM is found to provide an effective way for epileptic prediction. Furthermore, the resulting model is made autonomous using edge computing services and is shown to be a viable seizure prediction method. The results can be beneficial in real-life support of epilepsy patients.
Key words: Autonomous Edge Computing / Deep Learning / CNN / SVM / EEG / Epilepsy / Seizure Prediction / Brain-Computer Interface / Brain-Health Treatment
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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