Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. In this phase 2 validation, the algorithmic model performance of a contactless, marker-free video-based motor seizure detection system is evaluated against video-electroencephalographic monitoring (VEM) from two Danish epilepsy monitoring units. Detection performance of five categories of motor epileptic seizures (tonic-clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 seconds were assessed. 230 patients were recruited in the validation set, with 340 motor seizures identified by VEM. The control threshold was evaluated at a range of values to compare the sensitivity and false detection rate (FDR). At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/hr (CI): tonic-clonic- 95.2% (77.3%, 99.2%); 0.084 (0.081, 0.11), hyperkinetic- 92.9% (68.5%, 98.7%); 0.62 (0.57, 0.67), tonic- 78.3% (64.4%, 87.7%); 5.81 (5.46, 6.19), automatism- 87% (74.3%, 93.9%); 3.30 (3.08, 3.54), unclassified motor seizures- 77.7% (70.8%, 83.4%); 4.79 (4.48, 5.12), and PNES- 95.6% (85.2%, 98.8%); 1.70 (1.59, 1.83). A generic threshold recommended for all motor seizures under study asserted 87.8% sensitivity and 6.48 FDR/hr. These results indicate the system’s achievable performance at pre-specified thresholds that is clinically applicable for use as a seizure screening solution in diagnostic workflows.
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