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Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques

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Abstract

It is extremely significant to identify sleep stages accurately in the diagnosis of obstructive sleep apnea. In the study, it was aimed at determining sleep and wakefulness using a practical and applicable method. For this purpose , the signal of heart rate variability (HRV) has been derived from photoplethysmography (PPG). Feature extraction has been made from PPG and HRV signals. Afterward, the features, which will represent sleep and wakefulness in the best possible way, have been selected using F-score feature selection method. The selected features were classified with k-nearest neighbors classification algorithm and support vector machines. According to the results of the classification, the classification accuracy rate was found to be 73.36 %, sensivity 0.81, and specificity 0.77. Examining the performance of the classification, classifier kappa value was obtained as 0.59, area under an receiver operating characteristic value as 0.79, tenfold cross-validation as 77.35 %, and F-measurement value as 0.79. According to the results accomplished, it was concluded that PPG and HRV signals could be used for sleep staging process. It is a great advantage that PPG signal can be measured more practically compared to the other sleep staging signals used in the literature. Improving the systems, in which these signals will be used, will make diagnosis methods more practical.

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Acknowledgments

This research was supported by The Scientific and Technical Research Council of Turkey (TUBITAK) through The Research Support Programs Directorate (ARDEB) with project number of 115E657, and project name of “A New System for Diagnosing Obstructive Sleep Apnea Syndrome by Automatic Sleep Staging Using Photoplethysmography (PPG) Signals and Breathing Scoring” and by The Coordination Unit of Scientific Research Projects of Sakarya University. Produced from the doctoral thesis “Development of A New System for The Diagnosis of Sleep Staging and Sleep Apnea Syndrome” under the consultancy of the authors (Mehmet Recep Bozkurt), this study was supported by the SAU Commission of Scientific Research Projects (Project No: 2014-50-02-022). The ethics committee report numbered 16214662/050.01.04/70 from Sakarya University Deanship of Faculty of Medicine, and the data use permission numbered 94556916/904/151.5815 from T.C. Ministry of Health Turkey Public Hospitals Authority Sakarya Province General Secretariat of Association of Public Hospitals were received to perform the study.

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Authors and Affiliations

  1. Faculty of Engineering, Electrical-Electronic Engineering, Sakarya University, 54187, Sakarya, Turkey

    Muhammed Kürşad Uçar & Mehmet Recep Bozkurt

  2. Faculty of Medicine, Sakarya University, 54187, Sakarya, Turkey

    Cahit Bilgin

  3. Faculty of Engineering and Architecture, Electrical-Electronic Engineering, Abant İzzet Baysal University, 14280, Bolu, Turkey

    Kemal Polat

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  1. Muhammed Kürşad Uçar
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Correspondence to Muhammed Kürşad Uçar.

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Uçar, M.K., Bozkurt, M.R., Bilgin, C. et al. Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques. Neural Comput & Applic 29, 1–16 (2018). https://doi.org/10.1007/s00521-016-2365-x

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