fNIRS improves seizure detection in multimodal EEG-fNIRS recordings
| Autor: | Parikshat Sirpal, Ali Kassab, Frédéric Lesage, Philippe Pouliot, Dang Khoa Nguyen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Databases Factual Computer science seizure detection 02 engineering and technology Electroencephalography Epilepsy 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Diagnosis Computer-Assisted Brain Mapping Spectroscopy Near-Infrared medicine.diagnostic_test Artificial neural network functional brain imaging Signal Processing Computer-Assisted Middle Aged Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Memory Short-Term deep neural networks 020201 artificial intelligence & image processing Female Algorithms Paper Adult Adolescent Biomedical Engineering Context (language use) Biomaterials 03 medical and health sciences Young Adult Neuroimaging Seizures medicine Humans False Positive Reactions Aged Modality (human–computer interaction) business.industry Deep learning Hemodynamics Reproducibility of Results Pattern recognition medicine.disease Recurrent neural network Special Section on Metabolic Imaging and Spectroscopy: Britton Chance 105th Birthday Commemorative electroencephalography-functional near-infrared spectroscopy epilepsy Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery |
| Zdroj: | Journal of Biomedical Optics |
| ISSN: | 1560-2281 1083-3668 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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