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Bispectrum features and multilayer perceptron classifier to enhance seizure prediction

Elie Bou Assi, Laura Gagliano, Sandy Rihana, Dang K. Nguyen and Mohamad Sawan

Article (2018)

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Cite this document: Bou Assi, E., Gagliano, L., Rihana, S., Nguyen, D. K. & Sawan, M. (2018). Bispectrum features and multilayer perceptron classifier to enhance seizure prediction. Scientific Reports, 8. doi:10.1038/s41598-018-33969-9
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The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%.

Uncontrolled Keywords

Algorithms; Animals; Dogs; Humans; Neural Networks, Computer; Seizures, diagnosis; Statistics as Topic

Open Access document in PolyPublie
Subjects: 1900 Génie biomédical > 1900 Génie biomédical
2700 Technologie de l'information > 2713 Algorithmes
2700 Technologie de l'information > 2721 Systèmes et réseaux multimédias
Department: Département de génie électrique
Institut de génie biomédical
Research Center: Autre
Funders: CRSNG/NSERC, Epilepsy Canada, Institute for Data Valorization (IVADO)
Date Deposited: 12 May 2021 11:24
Last Modified: 13 May 2021 01:20
PolyPublie URL: https://publications.polymtl.ca/4800/
Document issued by the official publisher
Journal Title: Scientific Reports (vol. 8)
Publisher: Springer Nature
Official URL: https://doi.org/10.1038/s41598-018-33969-9


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