Sleep Stage Identification Using the Combination of ELM and PSO Based on ECG Signal and HRV

Abstract

The aim of this research was to build a classification model with an optimal accuracy to identify human sleep stages using Heart Rate Variability (HRV) features based on Electrocardiogram (ECG) signal. The proposed method is the combination of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) for feature selection and hidden node number determination. The combination of ELM and PSO produces mean of testing accuracy of 82.1 %, 76.77%, 71.52 %, and 62.66% for 2, 3, 4, and 6 number of classes respectively. This paper also provides comparison to ELM and Support Vector Machine (SVM) methods whose testing accuracy is lower than the combination of ELM and PSO. Based on the results, can be concluded that the addition of PSO method is able to increase classification performance.