Abstracts

Rationale: Purpose: Detection of generalized tonic-clonic seizures (GTCS) at home and during activities of daily living could facilitate earlier intervention. However, to date, there are no FDA-cleared devices that provide an accurate means for GTC seizure detection during activities of daily living. This study aims to validate a GTC seizure detection algorithm to be used later in a GTC seizure detection system to be cleared by the FDA. In order to do this, we (a) compared a software algorithm analysis of EMG signals continuously acquired in an Epilepsy Monitoring Unit (EMU) to video-EEG (VEEG) recordings, and, (b) optimized settings for software algorithm using baseline measurements of maximal strength and muscle contraction. Methods: Methods: Inpatient scalp VEEG monitoring was performed along with surface EMG recordings of unilateral biceps and triceps brachii. Twelve subjects suspected to have GTCS were admitted to the EMU for an average of 4.4 (range 3 to 6) days, providing over 372 hours of surface EMG data. The EMG data was analyzed separately by a seizure detection software algorithm (LGCH, Inc., Texas) and correlated with the determination of seizure onset and characterization by board-certified clinical neurophysiologists. The algorithm continuously compares recorded surface EMG signals to a baseline sample of muscle activity; GTCS are signaled with sustained activation of multiple frequency bands (30-40Hz, 130-240Hz, 300-400Hz) of EMG activity. The algorithm was expected to detect GTCS within 10 seconds of arm movement during the GTCS, while minimizing false positive detections. Results: Results: 6 of 12 subjects had a total of 7 GTCS captured by EMG recording and confirmed by VEEG. The EMG algorithm detected all 7 GTCS within 10 seconds of arm movement during the GTCS as identified by VEEG monitoring. There were no false positive detections. 69 myoclonic, 27 tonic, 12 absence and 14 complex partial seizures were recorded, but none triggered an alarm condition. No alarms were triggered during any activities of daily living or other interictal motor activities. Two subjects removed the EMG electrodes shortly before the end of their stay in the EMU due to discomfort. Conclusions: Conclusions: This interim view of a continuing study demonstrates the feasibility of accurately detecting GTC seizures with a minimally invasive, arm-worn device analyzing EMG signals. At this early point, the sensitivity and positive predictive value of the seizure detection algorithm appears to be superior to other devices currently under investigation or currently commercialized. The ability to reliably detect a GTCS provides an alert to caregivers and facilitates a more rapid response in order to minimize seizure related complications, including injuries and SUDEP. Comfort, wearability and ensuring a quality signal for analysis will be important factors in the design of the final system.