Detection of Epileptic Seizure Using Wireless Sensor Networks

Abstract

The monitoring of epileptic seizures is mainly done by means of EEG (Electroencephalogram) monitoring. Although this method is accurate, it is not comfortable for the patient as the EEG-electrodes have to be attached to the scalp which hampers the patient’s movement. This makes long term home monitoring not feasible.

In this paper the aim is to propose a seizure detection system based on accelerometry for the detection of epileptic seizure. The used sensors are wireless, which can improve quality of life for patients. In this system three 2D accelerometer sensors are positioned on right arm, left arm and left thigh of an epileptic patient. Datasets from three patients suffering from severe epilepsy are used in this paper for the development of an automatic detection algorithm.

This monitoring system is based on Wireless Sensor Networks that can determine the location of the patient when a seizure is detected and sends an alarm to hospital staff or their relatives. Our wireless sensor nodes are MICAz Motes developed by Crossbow Technology. The proposed system can be used for patients living in a clinical environment or their home, where they do only their daily routines.

The analysis of the recorded data is done by Artificial Neural Networks and K nearest-neighbor to recognize seizure movements from normal movements. The results show that the best algorithm for seizure detection is K Nearest Neighbor. We have shown that if at least 50 percent of signals consist of seizure samples, we can detect seizure accurately. Consequently, there is no need for training the algorithm for each new patient.