Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency and wavelet based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and

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... ileptic movement. This classification is only based on a non-parametric estimate of the probability density function of normal movements. Such approach allows to build patientspecific models to classify movement data without the need for seizure data that is rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure, otherwise it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95.24% and a Positive Predictive Value (PPV) of 60.04%. However, there is a noticeable inter-patient difference.