Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Epileptic Seizure Prediction
Eeg Data
Hybrid Cnn-Svm Model
Edge Computing Services
Machine Learning
Deep Learning

Author(s) Name:  Punjal Agarwal, Hwang-Cheng Wang and Kathiravan Srinivasan

Publisher name:  EDP Sciences

DOI:  10.1051/matecconf/201821003016

Volume Information:  Volume 210, 2018, Article Number 03016, Number of page(s):8

Paper Link:   https://www.matec-conferences.org/articles/matecconf/abs/2018/69/matecconf_cscc2018_03016/matecconf_cscc2018_03016.html