Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording

Zaid Bin Tariq, Arun Iyengar, Lara Marcuse, Hui Su, Bulent Yener

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Electroencephalogram (EEG) is a prominent way to measure the brain activity for studying epilepsy, thereby helping in predicting seizures. Seizure prediction is an active research area with many deep learning based approaches dominating the recent literature for solving this problem. But these models require a considerable number of patient-specific seizures to be recorded for extracting the preictal and interictal EEG data for training a classifier. The increase in sensitivity and specificity for seizure prediction using the machine learning models is noteworthy. However, the need for a significant number of patient-specific seizures and periodic retraining of the model because of non-stationary EEG creates difficulties for designing practical device for a patient. To mitigate this process, we propose a Siamese neural network based seizure prediction method that takes a wavelet transformed EEG tensor as an input with convolutional neural network (CNN) as the base network for detecting change-points in EEG. Compared to the solutions in the literature, which utilize days of EEG recordings, our method only needs one seizure for training which translates to less than ten minutes of preictal and interictal data while still getting comparable results to models which utilize multiple seizures for seizure prediction.

Original languageEnglish
Title of host publicationAI for Disease Surveillance and Pandemic Intelligence - Intelligent Disease Detection in Action
EditorsArash Shaban-Nejad, Martin Michalowski, Simone Bianco
PublisherSpringer Science and Business Media Deutschland GmbH
Pages295-308
Number of pages14
ISBN (Print)9783030930790
DOIs
StatePublished - 2022
Event5th International Workshop on Health Intelligence, W3PHAI 2021 held in conjection with 35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 8 Feb 20219 Feb 2021

Publication series

NameStudies in Computational Intelligence
Volume1013
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference5th International Workshop on Health Intelligence, W3PHAI 2021 held in conjection with 35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period8/02/219/02/21

Keywords

  • Deep learning
  • Electroencephalogram (EEG)
  • Seizure prediction
  • Siamese learning

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