Simple yet Effective Video-Based Epileptic Tonic-Clonic Seizure Detection

Epilepsy, a prevalent neurological disorder, often leads to tonic-clonic seizures characterized by loss of consciousness and uncontrolled motor activity. Prompt detection of these seizures is crucial for effective nursing and diagnosis. This paper introduces a novel epileptic seizure detection method leveraging low-complexity video analysis, eliminating the need for body attachments or special equipment like markers or specific clothing. Our approach is straightforward: each video frame is segmented into blocks, and the average values of these blocks are computed. We then analyze the temporal changes in these averages using spectrograms. Our findings indicate that during tonic-clonic seizures, dominant frequency components typically range from 1 to 6 Hz and decrease as the seizure progresses. By capitalizing on these clinical observations, we have formulated effective detection rules. Experimental evaluations reveal that our method not only accurately detects epileptic seizures but also operates approximately four times faster than real-time on standard desktop computers. This efficiency and accuracy underscore the potential of our method as a practical tool in epilepsy monitoring and management.