Detection of generalized tonic-clonic seizures using short length accelerometry signal

Abstract

Epileptic seizures are characterized by the excessive and abrupt electrical discharge in the brain. This asynchronous firing of neurons causes unprovoked convulsions which can be a cause of sudden unexpected death in epilepsy (SUDEP). Remote monitoring of epileptic patients can help prevent SUDEP. Systems based on wearable accelerometer sensors have shown to be effective in ambulatory monitoring of epileptic patients. However, these systems have a trade-off between seizure duration and the false alarm rate (FAR). The FAR of the system decreases as we increase the seizure duration. Further, multiple sensors are used in conjugation to improve the overall performance of the detection system. In this study, we propose a system based on single wrist-worn accelerometer sensor capable of detecting seizures with short duration (≥ 10s). Seizure detection was performed by employing machine learning approach such as kernelized support vector data description (SVDD). The proposed approach is validated on data collected from 12 patients, corresponding to approximately 966h of recording under video-telemetry unit. The algorithm resulted in a seizure detection sensitivity of 95.23% with a mean FAR of 0.72=24h.