Article found in Wiley Online Library
Objective: To evaluate the accuracy of a semiautomated classification of nocturnal seizures using a hybrid system consisting of an artificial intelligence-based algorithm, which selects epochs with potential clinical relevance to be reviewed by human experts.
Methods: Consecutive patients with nocturnal motor seizures admitted for video-EEG long-term monitoring (LTM) were prospectively recruited. We determined the extent of data reduction by using the algorithm, and we evaluated the accuracy of seizure-classification from the hybrid system compared with the gold standard of LTM.
Results: Forty consecutive patients (24 male; median age: 15 years) were analysed. The algorithm reduced the duration of epochs to be reviewed to 14% of the total recording time (1874 hours). There was a fair agreement beyond chance in seizure classification between the hybrid system and the gold standard (agreement coefficient: 0.33; 95% CI: 0.20-0.47). The hybrid system correctly identified all tonic-clonic and clonic seizures and 82% of focal motor seizures. However, there was low accuracy in identifying seizure types with more discrete or subtle motor phenomena.
Significance: Using a hybrid (algorithm-human) system for reviewing nocturnal video recordings significantly decreased the workload and provided accurate classification of major motor seizures (tonic-clonic, clonic and focal motor seizures).