Three-Dimensional Convolutional Neural Network Based Detection of Epileptic Seizures From Video Data

Abstract

Objective: Seizure detection in epilepsy monitoring units (EMU) is essential for successful seizure characterization and 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 novel approach, using a 3D convolutional neural network to identify seizures from EMU videos. Methods: A two-stream inflated 3D-ConvNet architecture classified video clips as a seizure or not a seizure. A pretrained action classification model was fine-tuned on 11 hours of video data 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-EEG review by an epileptologist on videos from site A and on a separate dataset from a second large academic hospital (site B). Results: The model classified previously unseen videos from site A with a mean accuracy of 0.944 +/- 0.064 and area under the receiver operating curve (AUC) score of 0.991 +/- 0.015. The mean latency between seizure onset and seizure detection was 0.61 +/- 2.01 seconds, which surpasses the prior state-of-the-art. A mean accuracy of 0.964 +/- 0.034 and AUC score of 0.984 +/- 0.049 were achieved by the model trained and tested at site B. Cross-site evaluation (site A model tested on site B data and vice versa) demonstrated generalizability of the model with a mean drop in AUC of -0.156. Significance: Our model demonstrates high performance in detection of epileptic seizures from video data using a spatiotemporal 3D-CNN models and outperforms each similar model we identified in a review of publications. This study provides a foundation for future work in real-time EMU seizure monitoring and possibly for reliable and cost-effective at-home detection of tonic-clonic seizures.