Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns

Article

Ramanna, S., Tirunagari, S. and Windridge, D. 2020. Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns. Health and Technology. 10 (3), pp. 699-709. https://doi.org/10.1007/s12553-019-00395-4
TypeArticle
TitleEpileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns
AuthorsRamanna, S., Tirunagari, S. and Windridge, D.
Abstract

Early detection of epileptic seizures has a significant impact on patient outcomes. A novel pipeline for EEG-based epileptic seizure detection is here presented in which frequency factorisation is carried out on EEG signals by using constrained Singular Spectrum Analysis (SSA), coupled with one dimensional Local Binary Patterns (1-D LBP). The resulting frequency pattern transformation is classified via a Support Vector Machine (SVM) using Half Total Error Rate (HTER) in order to evaluate the performance of the proposed pipeline in a class-imbalanced context, with results compared against 1-D LBP on reference datasets. The results are tested against other comparable pipelines and demonstrate best-in-class performance.

KeywordsEEG; Epilepsy; SSA; LBP; HTER
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
PublisherSpringer
JournalHealth and Technology
ISSN2190-7188
Electronic2190-7196
Publication dates
Online06 Feb 2020
PrintMay 2020
Publication process dates
Submitted15 Jul 2019
Accepted10 Nov 2019
Deposited10 May 2024
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1007/s12553-019-00395-4
Web of Science identifierWOS:000515954500002
LanguageEnglish
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