Automatic Video Detection of Nocturnal Epileptic Movement based on Motion Tracks

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%.