A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning

This is a prospective, blind comparison to the clinical gold standard for seizure characterization. This study is intended to compare the Nelli Software's ability to identify seizure events to vEEG review in adults with suspected nighttime seizures. Simultaneously, Nelli will continuously record audio and video while video-electroencephalography (vEEG) is recorded per typical standard of care. Events with positive motor manifestations will be independently identified, following standard clinical practice, by three epileptologists using clinical vEEG data. Nelli Software will review the audio and video data and independently identify events with positive motor manifestations. The outcomes of event identification will be compared between Epileptologists and the Nelli Software. For each category of event captured the positive percent agreement will be calculated using the exact binomial method. The primary endpoint of this study is to demonstrate that Nelli is able to identify seizures that have a positive motor component with a sensitivity of \> 70%.