Markerless video analysis for movement quantification in pediatric epilepsy monitoring

Abstract

Abstract-This paper proposes a markerless video analytic system for quantifying body part movements in pediatric epilepsy monitoring. The system utilizes colored pajamas worn by a patient in bed to extract body part movement trajectories, from which various features can be obtained for seizure detection and analysis. Hence, it is non-intrusive and it requires no sensor/marker to be attached to the patient's body. It takes raw video sequences as input and a simple user-initialization indicates the body parts to be examined. In background/foreground modeling, Gaussian mixture models are employed in conjunction with HSV-based modeling. Body part detection follows a coarse-to-fine paradigm with graphcut-based segmentation. Finally, body part parameters are estimated with domain knowledge guidance. Experimental studies are reported on sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection.

Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video

“…The multi-modal approach used by Conradsen et al [2] gives better results on patients with smaller seizures. Video based detection mainly focuses on the usage of markers [10] [1] or other ways to track limbs, like using colored pyjamas [4]. Karayiannis et al [6] didn't use any markers, but the moving limbs of the patients were clearly visible as they were monitored in the Neonatal Intensive Care Unit (NICU) of the hospital.…”