Selecting Features of Single Lead ECG Signal for Automatic Sleep Stages Classification using Correlation-based Feature Subset Selection

Abstract

Knowing about our sleep quality will help human life to maximize our life performance. ECG signal has potency to determine the sleep stages so that sleep quality can be measured. The data that used in this research is single lead ECG signal from the MIT-BIH Polysomnographic Database. The ECG’s features can be derived from RR interval, EDR information and raw ECG signal. Correlation-based Feature Subset Selection (CFS) is used to choose the features which are significant to determine the sleep stages. Those features will be evaluated using four different characteristic classifiers (Bayesian network, multilayer perceptron, IB1 and random forest). Performance evaluations by Bayesian network, IB1 and random forest show that CFS performs excellent. It can reduce the number of features significantly with small decreasing accuracy. The best classification result based on this research is a combination of the feature set derived from raw ECG signal and the random forest classifier.