Background Unsupervised nocturnal tonic‐clonic seizures (TCSs) may lead to sudden unexpected death in epilepsy (SUDEP). Major motor seizures (TCSs and hypermotor seizures) may lead to injuries. Our goal was to develop and validate an automated audio‐video system for the real‐time detection of major nocturnal motor seizures. Methods In this Phase‐3 clinical validation study, we assessed the performance of automated detection of nocturnal motor seizures using audio‐video streaming, computer vision and an artificial intelligence‐based algorithm (Nelli). The detection threshold was predefined, the validation dataset was independent from the training dataset, patients were prospectively recruited, and the analysis was performed in real time. The gold standard was based on expert evaluation of long‐term video electroencephalography (EEG). The primary outcome was the detection of nocturnal major motor seizures (TCSs and hypermotor seizures). The secondary outcome was the detection of other (minor) nocturnal motor seizures. Results We recruited 191 participants aged 1–72 years (median: 20 years), and we monitored them for 4183 h during the night. Device deficiency was present 10.5% of the time. Fifty‐one patients had nocturnal motor seizures during the recording. The sensitivity for the major motor seizures was 93.7% (95% confidence interval: 69.8%–99.8%). The system detected all 11 TCS and four out of five (80%) hypermotor seizures. For the minor motor seizure types, the sensitivity was low (8.3%). The false detection rate was 0.16 per h. Conclusion The Nelli system detects nocturnal major motor seizures with a high sensitivity and is suitable for implementation in institutions (hospitals, residential care facilities), where rapid interventions triggered by alarms can potentially reduce the risk of SUDEP and injuries.

Myoclonus in progressive myoclonus epilepsy type 1 (EPM1) patients shows marked variability, which presents a substantial challenge in devising treatment and conducting clinical trials. Consequently, fast and objective myoclonus quantification methods are needed. Methods: Ten video-recorded unified myoclonus rating scale (UMRS) myoclonus with action tests were performed on EPM1 patients who were selected for the development and testing of the automatic myoclonus quantification method. Human pose and body movement analyses of the videos were used to identify body keypoints and further analyze movement smoothness and speed. The automatic myoclonus rating scale (ARMS) was developed. It included the jerk count during movement score and the log dimensionless jerk (LDLJ) score to evaluate changes in the smoothness of movement. Results: The scores obtained with the automatic analyses showed moderate to strong significant correlation with the UMRS myoclonus with action scores. The jerk count of the primary keypoints and the LDLJ scores were effective in the evaluation of the myoclonic jerks during hand movements. They also correlated moderately to strongly with the total UMRS test panel scores (r 2 = 0,77, P = 0,009 for the jerk count score and r 2 = 0,88, P = 0,001 for the LDLJ score). The automatic analyses was weaker in quantification of the neck, trunk, and leg myoclonus. Conclusion: Automatic quantification of myoclonic jerks using human pose and body movement analysis of patients' videos is feasible and was found to be quite consistent with the accepted clinical gold standard quantification method. Based on the results of this study, the automatic analytical method should be further developed and validated to improve myoclonus severity follow-up for EPM1 patients.

No abstract