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.
Highlights
Continuous video-electroencephalography (EEG) surveillance is often used in hospitals to monitor patients at high-risk of epileptic seizures,[1] patients with drug-resistant chronic epilepsy admitted in epilepsy-monitoring units or critically ill patients admitted to the intensive care unit after an acute brain injury, such as stroke, head trauma, brain hemorrhage, or brain infection
This study focused on determining the potential of Functional near-infrared spectroscopy (fNIRS), a cost effective, portable neuroimaging technique in the detection of seizure events in multimodal EEG-fNIRS recordings
Our primary objective was to examine the enhanced capabilities that fNIRS signals provide for a seizure detection task, in particular when combined with EEG data in a multimodal framework, and our secondary objective was to utilize the power of neural networks for this task
Summary
Continuous video-electroencephalography (EEG) surveillance is often used in hospitals to monitor patients at high-risk of epileptic seizures,[1] patients with drug-resistant chronic epilepsy admitted in epilepsy-monitoring units or critically ill patients admitted to the intensive care unit after an acute brain injury, such as stroke, head trauma, brain hemorrhage, or brain infection. Nonconvulsive status epilepticus (defined as a continuous state of seizures without convulsions or multiple nonconvulsive seizures for more than 30 min without interictal full recovery) has been found to account for up to 20% of all cases of status epilepticus in general hospitals and up to 47% in the intensive care unit.[2] Functional near-infrared spectroscopy (fNIRS) has emerged as a safe and noninvasive optical technique that exploits neurovascular coupling to indirectly measure brain activity.
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