Authors: Ouday Hanosh, Rashid Ansari, Naoum P. Issa, A. Enis Cetin Description: Sudden Unexplained Death in Epilepsy (SUDEP) is a fatal threat to patients who suffer from convulsive seizures. The causes of the SUDEP are still ambiguous, and the patients who suffer from epileptic seizures may face death in well sleeping, likely after an unwitnessed convulsive seizure. An important step towards SUDEP prevention is reliable seizure detection during sleep that is inexpensive and unobtrusive. In this work, we developed a non-contact, non-intrusive, privacy-preserving system that can detect convulsive movements experienced by human subjects. Detection is accomplished by a combination of uncooled low-cost, low-power, low-resolution 8 x 8 IR array sensor, and a deep learning algorithm implemented with a Convolutional Neural Network (CNN). The thermopile sensor array is placed 1m from subjects who are reclining in bed. The CNN training set consists of thermal video streams from 40 healthy subjects mimicking convulsive movements or lying in bed without making convulsive movements. After training, the CNN was tested on thermal video streams not included in the training set and had a 99.2% accuracy in classifying convulsive movements and non-convulsive episodes, with no false negatives to distinguish between the occurrence and non-occurrence of convulsive movements. The performance results show that the thermopile sensor array has the potential to detect convulsive seizures while maintaining patient privacy and not requiring direct patient contact.