Abstract
Timely detection of seizures is crucial to implement optimal interventions, and may help reduce the risk of sudden unexpected death in epilepsy (SUDEP) in patients with generalized tonic-clonic seizures (GTCSs). While video-based automated seizure detection systems may be able to provide seizure alarms in both in-hospital and at-home settings, earlier studies have primarily employed hand-designed features for such a task. In contrast, deep learning-based approaches do not rely on prior feature selection and have demonstrated outstanding performance in many data classification tasks. Despite these advantages, neural network-based video classification has rarely been attempted for seizure detection. We here assessed the feasibility and efficacy of automated GTCSs detection from videos using deep learning. We retrospectively identified 76 GTCS videos from 37 participants who underwent long-term video-EEG monitoring (LTM) along with interictal video data from the same patients, and 10 full-night seizure-free recordings from additional patients. Using a leave-one-subject-out cross-validation approach (LOSO-CV), we evaluated the performance to detect seizures based on individual video frames (convolutional neural networks, CNNs) or video sequences [CNN+long short-term memory (LSTM) networks]. CNN+LSTM networks based on video sequences outperformed GTCS detection based on individual frames yielding a mean sensitivity of 88% and mean specificity of 92% across patients. The average detection latency after presumed clinical seizure onset was 22 seconds. Detection performance increased as a function of training dataset size. Collectively, we demonstrated that automated video-based GTCS detection with deep learning is feasible and efficacious. Deep learning-based methods may be able to overcome some limitations associated with traditional approaches using hand-crafted features, serve as a benchmark for future methods and analyses, and improve further with larger datasets.
Highlights
RELIABLE assessments of seizure frequency are essential for epilepsy diagnosis, syndrome evaluation, treatment selection, and prognosis
In patients with generalized tonic-clonic seizures (GTCSs), timely detection of seizures may be important in order to limit the risk of sudden unexpected death in epilepsy (SUDEP)[1]
Our results indicate that video-based GTCSs detection using deep learning without the need for feature-designing is feasible and outperforms approaches based on individual frames only
Summary
RELIABLE assessments of seizure frequency are essential for epilepsy diagnosis, syndrome evaluation, treatment selection, and prognosis. Video-EEG monitoring performed in specialized epilepsy monitoring units (EMUs) is the gold-standard in detecting and classifying epileptic seizures. This technique is time-and labor-consuming and only available at specialized centers. Automated methods to detect seizures, in particular GTCSs, may help improve patient monitoring and reduce the time and labor involved in screening and evaluating long-term video-EEG data in specialized EMU settings. Detection of seizures using video only, remains desirable since it does not require contact with the patient, and can be employed relatively including the use of potentially already existing video hardware in many settings
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
