Analyzing accelerometer data for epilepsy episode recognition

Abstract

Epilepsy is one of the main neurological disorders with high impact in the patient’s everyday life. An incorrect treatment or a lack in monitoring might produce cognitive damage and depression. Therefore, developing a wearable device for epilepsy monitoring would eventually complete the anamnesis, enhancing the medical staff diagnosing and treatment setting. This study shows the preliminary results in epilepsy onset recognition based on wearable tri-axial accelerometers and simple fuzzy set learnt using genetic algorithms. A complete experimentation for learning the fuzzy set is detailed. According to the obtained results, some generalized feasible solutions are discussed. Results show a very interesting researching area that might be easily transferred to embedded devices and online health care systems.