Detection of Nocturnal Epileptic Seizures from Wireless Inertial Measurements and Muscular Activity

The goal of this paper is to provide a lightweight approach for the early detection of epileptic seizures using data from inertial measurement unit and muscular activity. The detection procedure runs in a portable data collection device and raises an alarm for family member or other persons in the vicinity for fast assistance and eventually for saving the life of the monitored patient. The instantaneous power of sliding window is derived for inertial measurements from 3D Accelerometer (ACM), 3D Gyroscope (Gyro) and for the muscular activity from the ElectroMyoGram (EMG). The residual between forecasted and measured power is used as input for the detection algorithm based on Shewhart control chart. When the error between forecasted and derived power exceeds statistical chart limits [lower, upper] for several consecutive slots, an alarm is raised. The proposed approach is intended to improve the performance of existing detection systems by increasing the detection accuracy and reducing the false alarms through correlation analysis of collected data from 3D ACM, 3D Gyro and EMG. Our experimental results on real data set collected in Necker hospital from epileptic patients show that our proposed approach is robust against nocturnal movements and achieves a high level of detection accuracy with low false alarm rate.