Selecting Features of Single Lead ECG Signal for Automatic Sleep Stages Classification using Correlation-based Feature Subset Selection
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 ECGs 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.
Keywords: ECG features, Correlation-based Feature Subset Selection, RR interval, EDR, Raw ECG Signal, Sleep stages.
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ABOUT THE AUTHORS
Ary Noviyanto
Ary Noviyanto holds Bachelor degree in Computer Science from Department of Computer Science, Universitas Gadjah Mada, Indonesia. He is a research assistant for image processing and pattern recognition laboratory in faculty of computer science, Universitas Indonesia.
Sani M. Isa
Sani M. Isa holds Bachelor degree in Mathematics from Faculty of Natural Science, Padjadjaran University, Indonesia; and Master degree from Faculty of Computer Science, University of Indonesia.
Ito Wasito
Ito Wasito holds Ph.D in Computer Science, School of Computer Science and Information Systems, Birkbeck College, University of London, United Kingdom.
Aniati Murni Arymurthy
Aniati Murni Arymurthy holds Bachelor degree in Electrical Engineering from Faculty of Engineering, University of Indonesia, Master degree in Computer and Information Science, The Ohio State University, United States of America, and Doctor degree in Dept. Of Optoelectronics and Laser Application, University of Indonesia.
Ary Noviyanto
Ary Noviyanto holds Bachelor degree in Computer Science from Department of Computer Science, Universitas Gadjah Mada, Indonesia. He is a research assistant for image processing and pattern recognition laboratory in faculty of computer science, Universitas Indonesia.
Sani M. Isa
Sani M. Isa holds Bachelor degree in Mathematics from Faculty of Natural Science, Padjadjaran University, Indonesia; and Master degree from Faculty of Computer Science, University of Indonesia.
Ito Wasito
Ito Wasito holds Ph.D in Computer Science, School of Computer Science and Information Systems, Birkbeck College, University of London, United Kingdom.
Aniati Murni Arymurthy
Aniati Murni Arymurthy holds Bachelor degree in Electrical Engineering from Faculty of Engineering, University of Indonesia, Master degree in Computer and Information Science, The Ohio State University, United States of America, and Doctor degree in Dept. Of Optoelectronics and Laser Application, University of Indonesia.
