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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.

Author information

1
Université de Montréal, École Polytechnique de Montréal, Montréal, Québec, Canada.
2
Neurology Division, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada.
3
Montreal Heart Institute, Research Centre, Montreal, Québec, Canada.

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.

KEYWORDS:

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

PMID:
30734544
DOI:
10.1117/1.JBO.24.5.051408

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