An effective approach for epileptic seizures detection from multi-sensors integrated in an Armband

The main goal of this paper is to propose an effective approach for the early detection of nocturnal seizures from multi-signals gathered by an Armband. An inertial measurement unit and muscular activities acquisition sensors are integrated in the armband, and used to collect acceleration, angular velocity and electromyogram from the arm of the monitored patient. These signals are transmitted from the arm-band to a portable unit (e.g., SmartPhone) for preprocessing, detection and identification of seizures. Our approach starts by deriving the root mean square for acceleration and gyroscope, followed by the normalization of whole signals in the same range, and aggregation into one signal. The chart's control with its upper and lower limits are derived in the training phase (when the patient at rest) and used to detect abnormal seizures and to raise an alarm for patient relative for assistance to prevent further injuries when he loses consciousness.