AUTHOR=Rai Pragya , Knight Andrew , Hiillos Matias , Kertész Csaba , Morales Elizabeth , Terney Daniella , Larsen Sidsel Armand , Østerkjerhuus Tim , Peltola Jukka , Beniczky Sándor 

TITLE=Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 18 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2024.1324981

DOI=10.3389/fninf.2024.1324981

ISSN=1662-5196

ABSTRACT=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 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0-80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances 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 independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 seconds) motor seizures (out of 1114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n=81 subjects with seizures) and false detection rate (FDR) (n=all 230 subjects). At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/hr (CI): tonic-clonic-95.2% (82.4%, 100%); 0.09 (0.077, 0.103), hyperkinetic-92.9% (68.5%, 98.7%); 0.64 (0.59, 0.69), tonic-78.3% (64.4%, 87.7%); 5. 87 (5.51, 6.23), automatism-86.7% (73.5%, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures-78% (65.4%, 90.4%); 4. 81 (4.50, 5.14), and PNES-97.7% (97.7%, 100%); 1. 73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/hr. These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.