Detection of Epileptic Seizure Using Accelerometer Time Series Data and Hidden Markov Model

Epilepsy is one of the most prevalent neurological diseases globally, which causes seizures in the patient. As per a survey done worldwide, it is found that approximately 70 million people are living with epilepsy (~1% of the total population of the world). Effective detection of these seizures requires specialized approaches such as video and electroencephalography monitoring, which are expensive and are mainly available at specialized hospitals and institutes. Hence, there is a need to develop simpler and affordable systems that can be made available to health care centers and patients for accurate detection of epileptic seizures. A wireless remote monitoring system based on a wrist-worn accelerometer is an optimum choice for the same. Sophisticated algorithms need to be developed for effectively detecting seizure events from this accelerometer data with minimal false alarms. This paper presents a Hidden Markov Model (HMM) based probabilistic approach applied to the reduced-dimension feature vector representation of time-series accelerometer data to detect epileptic seizures. The results obtained from the HMM were compared with three commonly used machine learning models viz. support vector machine (SVM), logistic regression, and random forest. The proposed approach was able to detect 95.7% of seizures with a low false alarm rate of 14.8% with a run time of just under 24 seconds.