z-logo
open-access-imgOpen Access
Classification 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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here