Long-term accelerometry-triggered video monitoring and detection of tonic–clonic and clonic seizures in a home environment: Pilot study

Abstract

Purpose: The aim of our study was to test the efficacy of the VARIA system (video, accelerometry, and radar-induced activity recording) and validation of accelerometry-based detection algorithms for nocturnal tonic–clonic and clonic seizures developed by our team. Methods: We present the results of two patients with tonic–clonic and clonic seizures, measured for about one month in a home environment with four wireless accelerometers (ACM) attached to wrists and ankles. The algorithms were developed using wired ACM data synchronized with the gold standard video-/electroencephalography (EEG) and then run offline on the wireless ACM signals. Detection of seizures was compared with semicontinuous monitoring by professional caregivers (keeping an eye on multiple patients). Results: The best result for the two patients was obtained with the semipatient-specific algorithm which was developed using all patients with tonic–clonic and clonic seizures in our database with wired ACM. It gave a mean sensitivity of 66.87% and false detection rate of 1.16 per night. This included 13 extra seizures detected (31%) compared with professional caregivers' observations. Conclusion: While the algorithms were previously validated in a controlled video/EEG monitoring unit with wired sensors, we now show the first results of long-term, wireless testing in a home environment.

Keywords

• Epilepsy
• Patient-specific algorithm
• Nonpatient-specific algorithm
• Semipatient-specific algorithm
• Visual verification
• Data storage