Authors:
Kris Cuppens
1
;
Bert Bonroy
2
;
Anouk Van de Vel
3
;
Berten Ceulemans
3
;
Lieven Lagae
4
;
Tinne Tuytelaars
5
;
Sabine Van Huffel
6
and
Bart Vanrumste
1
Affiliations:
1
K. H. Kempen and KULeuven, Belgium
;
2
K. H. Kempen, Belgium
;
3
UZAntwerpen, Belgium
;
4
UZLeuven, Belgium
;
5
KULeuven, Belgium
;
6
KULeuven and KU Leuven, Belgium
Keyword(s):
Epilepsy, Seizure detection, Image motion analysis, Video monitoring, Optical flow, Mean shift clustering.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Monitoring and Telemetry
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Epileptic seizure detection in a home situation is often not feasible due to the complicated attachment of the EEG-electrodes on the scalp. We propose to detect nocturnal seizures with a motor component in patients by means of a single video camera. To this end we use a combination of optical flow and mean shift clustering to register moving body parts. After extraction of seven features, related to amplitude, duration and direction of the motion, we carry out a first validation with a linear support vector machine classifier. This resulted in a sensitivity of 80.60% and a positive predictive value of 62.07%.