Quantifying Limb Movements in Epileptic Seizures Through Color-Based Video Analysis

Abstract

This paper proposes a color-based video analytic system for quantifying limb movements in epileptic seizure monitoring. The system utilizes colored pyjamas to facilitate limb segmentation and tracking. Thus, it is unobtrusive and requires no sensor/marker attached to patient's body. We employ Gaussian mixture models in background/foreground modeling and detect limbs through a coarse-to-fine paradigm with graph-cut-based segmentation. Next, we estimate limb parameters with domain knowledge guidance and extract displacement and oscillation features from movement trajectories for seizure detection/analysis. We report studies on sequences captured in an epilepsy monitoring unit. Experimental evaluations show that the proposed system has achieved comparable performance to EEG-based systems in detecting motor seizures.

Automated seizure detection systems and their effectiveness for each type of seizure

“…Automatic video detection systems use velocity, area, duration, rotation, oscillation, angular speed, and/or displacement (motion trajectory) to detect seizures [93][94][95][96][97]. The underlying concept is to detect complex motor patterns by automatic interpretation of video data [93].…”

“…Seizure types that can be captured by video include focal, hypermotor, myoclonic, and clonic [93][94][95]98]. Myoclonic seizures are detected with good sensitivity and specificity with a marker-based system based on spatio-temporal interest points [95].…”

“…This was done with frontal and temporal lobe seizures to evaluate for the lateralizing value of ictal head turning [99,100]. The overall sensitivity varies from 75-100%, positive predictive value over 85%, and specificity of 53-93% [93][94][95][96]. Reported latencies range from 4.6 to 21.4 seconds [94].…”

“…The overall sensitivity varies from 75-100%, positive predictive value over 85%, and specificity of 53-93% [93][94][95][96]. Reported latencies range from 4.6 to 21.4 seconds [94].…”

“…Marker-based devices present with the shortcoming that sensors can be uncomfortable or dislocate over time [94,97]. Marker-free systems detect only seizures with a motor component, and they are more limited to the area covered by video: the patient must be visible and properly placed [93].…”