An integrative method to quantitatively detect nocturnal motor seizures

Abstract

In this proof-of-concept investigation, we demonstrate a marker-free video-based method to detect nocturnal motor seizures across a spectrum of motor seizure types, in a nighttime setting with a single adult female with refractory epilepsy. In doing so, we further explore the intermediate biosignals, visually mapping seizure “fingerprints” to seizure types. The method is designed to be flexible enough to generalize to unseen data, and shows promising performance characteristics for low-cost seizure detection and classification. The dataset contained recordings from 27 recorded nights. Seizure events were observed in 22 of these nights, with 36 unequivocally confirmed seizures. Each seizure was classified by an expert epileptologist according to both the ILAE 2017 standard and the Lüders semiological classification guidelines, yielding 5 of the ILAE-recognized seizure types and 7 distinct seizure semiologies. Evaluation was based on inference of motion, oscillation, and sound signals extracted from the recordings. The model architecture consisted of two feature extraction and event determination layers and one thresholding layer, establishing a simple framework for multimodal seizure analysis. Training of the optimal parameters for was done by randomly resampling the event hits for each signal, and choosing a threshold that kept an expected 90% sensitivity for the sample distribution. With the cut-off values selected, statistical performance was calculated for two target seizure groups: those containing a clonic component, and those containing a tonic component. When tuned to 90% sensitivity, the system achieved a very low false discovery rate of 0.038/hour when targeting seizures with a clonic component, and a clinically-relevant rate of 1.02/hour when targeting seizures with a tonic component. These results indicate a sensitive method for detecting various nocturnal motor seizure types, and a high potential to differentiate motor seizures based on their video and audio signal characteristics. Paired with the low cost of this technique, both cost savings and improved quality of care might be achieved through further development and commercialization of this method.