Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives

Emran Ali, Maia Angelova, Chandan Karmakar

Research output: Contribution to journalArticlepeer-review

Abstract

Epilepsy is a life-threatening neurological condition. Manual detection of epileptic seizures (ES) is laborious and burdensome. Machine learning techniques applied to electroencephalography (EEG) signals are widely used for automatic seizure detection. Some key factors are worth considering for the real-world applicability of such systems: (i) continuous EEG data typically has a higher class imbalance; (ii) higher variability across subjects is present in physiological signals such as EEG; and (iii) seizure event detection is more practical than random segment detection. Most prior studies failed to address these crucial factors altogether for seizure detection. In this study, we intend to investigate a generalized cross-subject seizure event detection system using the continuous EEG signals from the CHB-MIT dataset that considers all these overlooked aspects. A 5-second non-overlapping window is used to extract 92 features from 22 EEG channels; however, the most significant 32 features from each channel are used in experimentation. Seizure classification is done using a Random Forest (RF) classifier for segment detection, followed by a post-processing method used for event detection. Adopting all the above-mentioned essential aspects, the proposed event detection system achieved 72.63% and 75.34% sensitivity for subject-wise 5-fold and leave-one-out analyses, respectively. This study presents the real-world scenario for ES event detectors and furthers the understanding of such detection systems.
Original languageEnglish
Article number230601
Number of pages19
JournalRoyal Society Open Science
Volume11
Issue number5
Early online date29 May 2024
DOIs
Publication statusPublished - May 2024

Bibliographical note

Copyright © 2024, The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

Data Access Statement

Used data are publicly available on Physionet: https://doi.org/10.13026/C2K01R, under license: Open Data Commons Attribution License v1.0: https://www.physionet.org/content/chbmit/view-license/1.0.0/. According to the License terms in section 3.0 'Rights granted' subsections—a, b, d and e; the following rights are granted to the users: Extraction and re-utilization; creation of derivative databases; creation of temporary or permanent reproductions; distribution, communication, display, lending, making available or performance to the public by any means and in any form, in whole or in part. Electronic supplementary material is available online at [https://rs.figshare.com/collections/Supplementary_material_from_Epileptic_Seizure_Detection_Using_CHB-MIT_Dataset_The_Overlooked_Perspectives_/7184012].

Keywords

  • epilepsy
  • seizure
  • machine learning
  • seizure event detection
  • cross-subject analysis
  • health informatics

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