Automated detection of convulsive seizures using a wearable accelerometer device

Epileptic seizure detection requires specialized approaches such as video/electroencephalography monitoring. However, these approaches are restricted mainly to hospital setting and requires video/EEG analysis by experts, which makes these approaches resource- and labor-intensive. In contrast, we aim to develop a wireless remote monitoring system based on a single wrist-worn accelerometer device, which is sensitive to multiple types of convulsive seizures and is capable of detecting seizures with short duration. Simple time domain features including a new set of Poincaré plot based features were extracted from the active movement events recorded using a wrist-worn accelerometer device. The best features were then selected using the area under the ROC curve analysis. Kernelized support vector data description was then used to classify nonseizure and seizure events. The proposed algorithm was evaluated on 5576 h of recordings from 79 patients and detected 40 (86.95%) of 46 convulsive seizures (generalized tonic-clonic (GTCS), psychogenic nonepileptic, and complex partial seizures) from 20 patients with a total of 270 false alarms (1.16/24 h). Furthermore, the algorithm showed a comparable performance (sensitivity 95.23% and false alarm rate 0.64/24 h) with respect to existing unimodal and multimodal methods for GTCS detection. The promising results shows the potential to build an ambulatory monitoring convulsive seizure detection system. A wearable accelerometer based seizure detection system would aid in continuous assessment of convulsive seizures in a timely and non-invasive manner.