Epileptic Seizures Detection based on Inertial and Physiological Data from Wireless Body Sensors

In this paper, we propose a novel model for real-time detection of nocturnal epileptic seizures from physiological and inertial data, which are collected by a wireless wristband with integrated muscular activity sensors, 3D accelerometer and 3D gyroscope. The wristband transmits the data to a portable unit for processing and seizures detection. It must be able to distinguish normal nocturnal movements from seizures, and to raise an alarm upon detection of seizures for patient relatives and for predefined contacts. Our real-time detection model starts by reducing the dimensionality of collected data through the use of root mean square to derive one signal from 3D accelerometer and one signal from 3D gyroscope. With the derived 3 signals (accelerometer, gyroscope and electromyogram), we apply the vector triple product to derive one signal used as input for anomaly detection mechanism. The robust version of z-score is applied on the resulting product signal to detect deviations associated with seizures before raising an alarm for patient relative for assistance to prevent further injuries when the patient loses control with the excessive discharge of neurons.