Epilepsia · 2025
Video-based detection of tonic-clonic seizures using a three-dimensional convolutional neural network.
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Boyne Aidan, Yeh Hsiang J, Allam Anthony K, Brown Brandon M, Tabaeizadeh Mohammad, Stern John M, Cotton R James, Haneef Zulfi
Аннотация
OBJECTIVE: Seizure detection in epilepsy monitoring units (EMUs) is essential for the clinical assessment of drug-resistant epilepsy. Automated video analysis using machine learning provides a promising aid for seizure detection, with resultant reduction in the resources required for diagnostic monitoring. We employ a three-dimensional (3D) convolutional neural network with fully fine-tuned backbone layers to identify seizures from EMU videos.
METHODS: A two-stream inflated 3D-ConvNet architecture (I3D) classified video clips as a seizure or not a seizure. A pretrained action classifier was fine-tuned on 11 h of video containing 49 tonic-clonic seizures from 25 patients monitored at a large academic hospital (site A) using leave-one-patient-out cross-validation. Performance was evaluated by comparing model predictions to ground-truth annotations obtained from video-electroencephalographic review by an epileptologist on videos from site A and a separate dataset from a second large academic hospital (site B).
RESULTS: The model achieved a leave-one-patient-out cross-validation F1-score of .960 ± .007 (mean ± SD) and area under the receiver operating curve score of .988 ± .004 at site A. Evaluation on full videos detected all seizures (95% binomial exact confidence interval = 94.1%-100%), with median detection latency of 0.0 s (interquartile range = 0.0-3.0) from seizure onset. The site A model had an average false alarm rate of 1.81 alarms per hour, although 36 of the 49 videos (73.5%) had no false alarms. Evaluation at site B demonstrated generalizability of the architecture and training strategy, although cross-site evaluation (site A model tested on site B data and vice versa) resulted in diminished performance.
SIGNIFICANCE: Our model demonstrates high performance in the detection of epileptic seizures from video data using a fine-tuned I3D model and outperforms similar models identified in the literature. This study provides a foundation for future work in real-time EMU seizure monitoring and possibly for reliable, cost-effective at-home detection of tonic-clonic seizures.
METHODS: A two-stream inflated 3D-ConvNet architecture (I3D) classified video clips as a seizure or not a seizure. A pretrained action classifier was fine-tuned on 11 h of video containing 49 tonic-clonic seizures from 25 patients monitored at a large academic hospital (site A) using leave-one-patient-out cross-validation. Performance was evaluated by comparing model predictions to ground-truth annotations obtained from video-electroencephalographic review by an epileptologist on videos from site A and a separate dataset from a second large academic hospital (site B).
RESULTS: The model achieved a leave-one-patient-out cross-validation F1-score of .960 ± .007 (mean ± SD) and area under the receiver operating curve score of .988 ± .004 at site A. Evaluation on full videos detected all seizures (95% binomial exact confidence interval = 94.1%-100%), with median detection latency of 0.0 s (interquartile range = 0.0-3.0) from seizure onset. The site A model had an average false alarm rate of 1.81 alarms per hour, although 36 of the 49 videos (73.5%) had no false alarms. Evaluation at site B demonstrated generalizability of the architecture and training strategy, although cross-site evaluation (site A model tested on site B data and vice versa) resulted in diminished performance.
SIGNIFICANCE: Our model demonstrates high performance in the detection of epileptic seizures from video data using a fine-tuned I3D model and outperforms similar models identified in the literature. This study provides a foundation for future work in real-time EMU seizure monitoring and possibly for reliable, cost-effective at-home detection of tonic-clonic seizures.