Open AccessClassification of functional Near Infra Red Signals with Machine Learning for Prediction of Epilepsy
Author(s) -
Roberto Rosas Romero,
Edgar Guevara
Publication year - 2020
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2398-7340
DOI - 10.29007/qqx8
Subject(s) - support vector machine , artificial intelligence , epilepsy , convolutional neural network , computer science , electroencephalography , functional near infrared spectroscopy , epileptic seizure , classifier (uml) , pattern recognition (psychology) , artificial neural network , deep learning , machine learning , speech recognition , neuroscience , psychology , cognition , prefrontal cortex
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.