Download PDFOpen PDF in browser

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

8 pagesPublished: 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.

BibTeX entry
@inproceedings{BICOB2020:Classification_functional_Near_Infra,
  author    = {Roberto Rosas Romero and Edgar Guevara},
  title     = {Classification of functional Near Infra Red Signals with Machine Learning for Prediction of Epilepsy},
  booktitle = {Proceedings of the 12th International Conference on Bioinformatics and Computational Biology},
  editor    = {Qin Ding and Oliver Eulenstein and Hisham Al-Mubaid},
  series    = {EPiC Series in Computing},
  volume    = {70},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/5K9x},
  doi       = {10.29007/qqx8},
  pages     = {41-48},
  year      = {2020}}
Download PDFOpen PDF in browser