Classification of functional Near Infra Red Signals with Machine Learning for Prediction of Epilepsy

8 pages•Published: March 11, 2020

Abstract

This work presents the classification of functional near-infrared spectroscopy (fNIRS) signals as a tool for prediction of epileptic seizures. The implementation of epilepsy prediction is accomplished by using two classifiers, namely a Support Vector Machine (SVM) for EEG-based prediction and a Convolutional Neural Network (CNN) for fNIRS-based prediction. Performance was measured by computing the Positive Predictive Value (PPV) and the Accuracy of a classifier within a 5-minute window adjacent and previous to the start of the seizure. The objectives of this research are to show that fNIRS-based epileptic seizure prediction yields results that are superior to those based on EEG and to show how deep learning is applied to the solution of this problem.

Keyphrases: convolutional neural network, electroencephalogram, epileptic seizure prediction, functional near infra red spectroscopy, support vector machine

In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 41-48.

Links:	https://easychair.org/publications/paper/5K9x
	https://doi.org/10.29007/qqx8