<  Back to the Polytechnique Montréal portal

fNIRS improves seizure detection in multimodal EEG-fNIRS recordings

Parikshat Sirpal, Ali Kassab, Philippe Pouliot, Dang Khoa Nguyen, Frédéric Lesage

Article (2019)

Open Acess document in PolyPublie and at official publisher
[img]
Preview
Open Access to the full text of this document
Published Version
Terms of Use: Creative Commons Attribution
Download (1MB)
Show abstract
Hide abstract

Abstract

In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database-a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.

Uncontrolled Keywords

*deep neural networks; *electroencephalography-functional near-infrared spectroscopy; *epilepsy; *functional brain imaging; *seizure detection

Subjects: 2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering
6400 Life sciences research related to human health and disease > 6400 Life sciences research related to human health and disease
Department: Department of Electrical Engineering
Research Center: Other
Funders: Natural Sciences and Engineering Research Council of Canada, Canadian Institutes of Health Research
Grant number: RGPIN-2017-06140, 396317
PolyPublie URL: https://publications.polymtl.ca/5138/
Journal Title: Journal of Biomedical Optics (vol. 24, no. 5)
Publisher: SPIE
DOI: 10.1117/1.jbo.24.5.051408
Official URL: https://doi.org/10.1117/1.jbo.24.5.051408
Date Deposited: 13 Jul 2022 11:00
Last Modified: 11 Nov 2022 13:27
Cite in APA 7: Sirpal, P., Kassab, A., Pouliot, P., Nguyen, D. K., & Lesage, F. (2019). fNIRS improves seizure detection in multimodal EEG-fNIRS recordings. Journal of Biomedical Optics, 24(5), 051408 (9 pages). https://doi.org/10.1117/1.jbo.24.5.051408

Statistics

Total downloads

Downloads per month in the last year

Origin of downloads

Dimensions

Repository Staff Only

View Item View Item