• Jul 29, 2019 News!IJMLC Had Implemented Online Submission System, Please Sumbit New Submissions thorough This System Only!   [Click]
  • Jul 16, 2019 News!Good News! All papers from Volume 9, Number 3 have been indexed by Scopus!   [Click]
  • Jul 08, 2019 News!Vol.9, No.4 has been published with online version.   [Click]
General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2019 Vol.9(4): 520-526 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.4.835

An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

Thakerng Wongsirichot, Nittida Elz, Supasit Kajkamhaeng, Wanchai Nupinit, and Narongrit Sridonthong
Abstract—Sleep quality is highly significant for the people’s overall health. A standard diagnosis for sleep-related syndromes and illnesses is Polysomnography (PSG) or a sleep test in a controlled laboratory. However, PSG requires a sleep specialist to interpret bio-signals collected. It is a time consuming procedure. One of the fundamental step in the PSG is Sleep Stage Classification (SSC). In this study, we propose an investigation of Automatic Sleep Stage Classification (ASSC) using data mining techniques as an alternative to the PSG in order to reduce the time necessary for accurately diagnosing sleep quality. We studied 2,535 subjects’ polysomnographic data with 14 channels of biomedical signals from the Sleep Heart Health Study (SHHS) Dataset. Subsequently, four data mining techniques including Decision Trees, Random Forests, Neural Network, and k-Nearest Neighbors were selected to compare the classification performances. The classification results in k-Nearest Neighbors achieved the greatest accuracy at 83.76%.

Index Terms—Automatic sleep stages classification, ASSC, sleep quality, data mining.

The authors are with the Department of Information and Communication Technology, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, Thailand (e-mail: thakerng.w@psu.ac.th, nittida.n@psu.ac.th, supasit.k@psu.ac.th,wanchai.23.chai@gmail.com, iamnarongrit@gmail.com).


Cite: Thakerng Wongsirichot, Nittida Elz, Supasit Kajkamhaeng, Wanchai Nupinit, and Narongrit Sridonthong, "An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 520-526, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Copyright © 2008-2019. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net