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Automated Detection and Prediction of Sleep Apnea Events

Shewinvanakitkul, Prapan
http://rave.ohiolink.edu/etdc/view?acc_num=case1486490112558014

Abstract Details

2017, Doctor of Philosophy, Case Western Reserve University, EECS - System and Control Engineering.
This thesis describes a system that can be used for accurate detection and prediction of sleep apnea events by employing signal pre-processing, feature extraction and classification techniques. Sleep apnea syndrome is characterized by the repeated temporary cessation of breathing to the lungs during sleep – an important health problem that can lead to reduced daytime work performance and accidents. Some studies have linked sleep apnea to atrial fibrillation, stroke, myocardial infarction and sudden cardiac death. An important and practical problem is therefore the processing of biomedical signals to extract information that reflect characteristics of sleep apnea. A diagnosis of sleep apnea typically requires full-night multi-channel monitoring by means of overnight polysomnography (PSG). This research addresses the real-time problem of both classifying sleep apnea events and predicting impending apnea events using PSG data available prior to these events. In this study we examined differences between patients who have sleep apnea but are non-hypertensive and those that have sleep apnea but are hypertensive. These patient groups were found to have different characteristics in terms of how they were needed to be handled for accurate detection and prediction of sleep apnea events. Experimental results demonstrate the excellent flexibility of the proposed sleep apnea detection and sleep apnea prediction algorithms in term of accuracy for both groups. As a result, the detection and prediction of individual episodes of sleep apnea is approached using several algorithms that offer promise to reduce health care cost related to treatment.
Marc Buchner, PhD (Advisor)
Kenneth Loparo, PhD (Committee Member)
Vira Chankong, PhD (Committee Member)
Frank Jacono, MD (Committee Member)
109 p.
Artificial Intelligence; Biomedical Engineering; Statistics; Systems Design
Predictive modeling; Classification; Signal processing; Sleep Apnea

Recommended Citations

Citations

  • Shewinvanakitkul, P. (2017). Automated Detection and Prediction of Sleep Apnea Events [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1486490112558014

    APA Style (7th edition)

  • Shewinvanakitkul, Prapan. Automated Detection and Prediction of Sleep Apnea Events. 2017. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1486490112558014.

    MLA Style (8th edition)

  • Shewinvanakitkul, Prapan. "Automated Detection and Prediction of Sleep Apnea Events." Doctoral dissertation, Case Western Reserve University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1486490112558014

    Chicago Manual of Style (17th edition)

case1486490112558014
1,665
© 2017, some rights reserved.Creative Commons License Automated Detection and Prediction of Sleep Apnea Events by Prapan Shewinvanakitkul is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.