Non-intrusive head movement analysis of videotaped seizures of epileptic origin.

In this work we propose a non-intrusive video analytic system for patient's body parts movement analysis in Epilepsy Monitoring Unit. The system utilizes skin color modeling, head/face pose template matching and face detection to analyze and quantify the head movements. Epileptic patients' heads are analyzed holistically to infer seizure and normal random movements. The patient does not require to wear any special clothing, markers or sensors, hence it is totally non-intrusive. The user initializes the person-specific skin color and selects few face/head poses in the initial few frames. The system then tracks the head/face and extracts spatio-temporal features. Support vector machines are then used on these features to classify seizure-like movements from normal random movements. Experiments are performed on numerous long hour video 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.

Markerless video analysis for movement quantification in pediatric epilepsy monitoring.

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 graph-cut-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.

Pediatric epilepsy and first afebrile seizure in Singapore: epidemiology and investigation yield at presentation.

The authors studied pediatric epilepsy and first afebrile seizure at presentation in Singapore. A total of 211 participants aged 1 month to 15 years with first presentation for afebrile seizures were recruited from November 2002 to May 2004; 108 with ≥2 prior afebrile seizures (newly diagnosed epilepsy) and 103 with first afebrile seizures. A χ(2) analysis of demographics, risk factors, examination, and investigation findings showed significant differences in development (normal in 87% [newly diagnosed epilepsy] and 93% [first afebrile seizure], P = .046), neurological examination (normal in 92% [newly diagnosed epilepsy] and 98% [first afebrile seizure], P = .016), and electroencephalogram findings (abnormal in 75% [newly diagnosed epilepsy] and 36.9% [first afebrile seizure], P < .005). Pediatric epilepsy incidence at our institution is 24 per 100 000 person-years and is highest in early childhood. Focal epilepsy is more common than generalized epilepsy. Patients with first afebrile seizure and abnormal development, neurological examination, and electroencephalogram findings should be monitored for future development of epilepsy. Population-based studies are recommended.