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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

Links:
BibTeX entry
@inproceedings{BICOB2020:Classification_of_functional_Near,
  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},
  pages     = {41--48},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/5K9x},
  doi       = {10.29007/qqx8}}
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