Detection of nocturnal epileptic seizures using wireless 3-D accelerometer sensors

The aim of this paper is to provide a lightweight approach for early detection of nocturnal epileptic seizures using data from wireless 3-D accelerometer sensors. We use the exponentially weighted moving average algorithm to forecast the current value of the accelerometer measurement, and when the difference between measured and forecasted values is greater than the dynamic threshold on any axis, a notification is transmitted to the base station, which maintains a sliding window of received notifications. When the filling ratio is greater than a predefined threshold, an alarm is triggered by the base station. The proposed approach is intended to improve the performance of existing mobile health detection systems based on the analysis of electroencephalogram (EEG). To reduce their false alarm rate, we seek to correlate detection results from 3-D accelerometer with other physiological parameters through a majority voting. Our experimental results on real dataset collected from the epileptic patient show that our proposed approach is robust against temporal fluctuations and achieves a high level of detection accuracy, which in turn proves the effectiveness of this approach in enhancing the reliability of existing detection approaches based on EEG signal analysis.