Elie Bou Assi, Laura Gagliano, Sandy Rihana, Dang K. Nguyen and Mohamad Sawan
Article (2018)
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Published Version Terms of Use: Creative Commons Attribution . Download (1MB) |
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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Abstract
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
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Subjects: |
1900 Génie biomédical > 1900 Génie biomédical 2700 Technologie de l'information > 2713 Algorithmes 2700 Technologie de l'information > 2721 Systèmes et réseaux multimédias |
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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/ |
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Journal Title: | Scientific Reports (vol. 8) |
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Publisher: | Springer Nature |
Official URL: | https://doi.org/10.1038/s41598-018-33969-9 |
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