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J Biomed Opt. 2019 Feb;24(5):1-9. doi: 10.1117/1.JBO.24.5.051408.

fNIRS improves seizure detection in multimodal EEG-fNIRS recordings.

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Université de Montréal, École Polytechnique de Montréal, Montréal, Québec, Canada.
Neurology Division, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada.
Montreal Heart Institute, Research Centre, Montreal, Québec, Canada.


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.


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


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