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It is common that epileptic seizures induce uncoordinated movement in a patient's body. This movement is a relevant clinical factor in seizure identification. Nevertheless, quantification of this information has not been an object of much attention from the scientific community. In this paper, we present our effort in developing a new approach to the quantification of movement patterns in patients during epileptic seizures. We attach markers at landmark points of a patient's body and use a camera and a commercial video-electroencephalogram (EEG) system to synchronously register EEG and video during seizures. Then, we apply image-processing techniques to analyze the video frames and extract the trajectories of those points that represent the course of the quantified movement of different body parts. This information may help clinicians in seizure classification. We describe the framework of our system and a method of analyzing video in order to achieve the proposed goal. Our experimental results show that our method can reflect quantified motion patterns of epileptic seizures, which cannot be accessed by means of traditional visual inspection of video recordings. We were able, for the first time, to quantify the movement of different parts of a convulsive human body in the course of an epileptic seizure. This result represents an enhanced value to clinicians in studying seizures for reaching a diagnosis.