In this paper, three triaxis accelerometers positioned on the wrists and the head of epileptic patients submitted to long-term video electroencephalographic monitoring as part of presurgical investigation are evaluated to characterize the different classes of motor manifestations observed during seizures. Quadratic discriminant classifiers are trained on features extracted from 1 or 4 s windows. It is shown that a simple rule applied to the acceleration norm entropy HnA produces the best performances compared to other classifiers trained on other feature sets. The simple rule is as follows with values given in bits: (0