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An automated framework for predicting obstructive sleep apnea using a brief, daytime, non-intrusive test procedure

Published: 01 July 2015 Publication History

Abstract

Sleep constitutes a big portion of our lives and is a major part of health and well-being. The vital repair and regeneration tasks carried out during sleep are essential for our physical, mental and emotional health. Obstructive sleep apnea (OSA) is a sleep disorder that is characterized by repeated pauses in breathing during sleep. These pauses, or apneas, deplete the brain and the rest of the body of oxygen and disrupt the normal sleep cycle. OSA is associated with a number of adverse safety and health consequences, including excessive daytime sleepiness and fatigue, which increase the risk for motor vehicle and work-related accidents. OSA also results in an increased risk for hypertension, cardiovascular disease, the development of diabetes and even premature death. The gold standard method for diagnosing OSA patients is polysomnography (PSG). PSG is an overnight sleep test that monitors a participant's biophysical changes (EEG, ECG, etc.) that occur during sleep. Despite its wide use and multi-parametric nature, there are multiple complications associated with that test that make it ineffective as an early-stage diagnosis tool. In this paper, we propose a daytime OSA screening tool that addresses the shortcomings of PSG. The framework consists of a data collection component that acquires information about the subject being tested, and a prediction component that analyzes the collected data and makes a diagnosis.
We identify patients' key physiological, psychological and contextual features and apply advanced machine learning algorithms to build effective prediction models that help identify OSA patients in the comfort of their own home. The system was deployed in a pilot sleep apnea study of 16 patients. Results demonstrate the proposed system's great potential in helping sleep specialists in the initial assessment of patients with suspected OSA.

References

[1]
Chronic respiratory diseases. http://www.who.int/gard/publications/chronic_respiratory_diseases.pdf.
[2]
Obstructive sleep apnea. http://www.aasmnet.org/resources/factsheets/sleepapnea.pdf, 2008.
[3]
V. K. abd D. K. Blough, R. Sandblom, R. Hert, J. de Maine, S. Sullivan, and B. Psaty. The medical cost of undiagnosed sleep apnea. SLEEP, 22(6):749--755, Sep 1999.
[4]
S. Alqassim, M. Ganesh, S. Khoja, M. Zaidi, F. Aloul, and A. Sagahyroon. Sleep apnea monitoring using mobile phones. In IEEE International Conference on e-Health Networking, Applications and Services, 2012.
[5]
J. Behar, A. Roebuck, M. Shahid, and J. Daly. Sleepap: An automated obstructive sleep apnoea screening application for smartphones. IEEE Journal of Biomedical and Health Informatics, 19(1):325--331, Feb 2014.
[6]
D. Buysse, C. R. III, T. Monk, S. Berman, and D. Kupfer. The pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Journal of Psychiatric Research, 28(2):193--213, 1989.
[7]
S. Cohen, T. Kamarck, and R. Mermelstein. A global measure of perceived stress. Journal of Health and Social Behavior, 24(4):385--396, Dec 1983.
[8]
M. Huang, W. Xu, J. Liu, L. Samy, A. Vajid, N. Alshurafa, and M. Sarrafzadeh. Inconspicuous on-bed respiratory rate monitoring. In International Conference on PErvasive Technologies Related to Assistive Environments, 2013.
[9]
S. M. Isa, M. Fanany, W. Jatmiko, and A. Murni. Feature and model selection on automatic sleep apnea detection using ecg. In International Conference on Advanced Computer Science and Information Systems, 2010.
[10]
A. Khandoker, M. Palaniswami, and C. Karmakar. Support vector machines for automated recognition of obstructive sleep apnea syndrome from ecg recordings. IEEE Transactions on Information Technology in Biomedicine, 13(1):37--48, Jan 2009.
[11]
K. Kroenke, R. Spitzer, J. Williams, and B. Lowe. An ultra-brief screening scale for anxiety and depression: the phq-4. Psychosomatics, 50(6):613--621, 2009.
[12]
N. A. Lawati, S. Patel, and N. Ayas. Epidemiology, risk factors, and consequences of obstructive sleep apnea and short sleep duration. Progress in Cardiovascular Diseases, 51(4):285--293, 2009.
[13]
D. Liu, Z. Pang, and S. R. Lloyd. A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and eeg. IEEE Transactions on Neural Networks, 19(2):308--318, Feb 2008.
[14]
P. Macey, R. Kumar, J. Ogren, M. Woo, and R. Harper. Global brain blood-oxygen level responses to autonomic challenges in obstructive sleep apnea. PLoS One, Aug 2014.
[15]
P. Macey, R. Kumar, M. Woo, E. Valladares, F. Yan-Go, and R. Harper. Brain structural changes in obstructive sleep apnea. Sleep, 31(7):967--977, Jul 2008.
[16]
P. Macey, R. Kumar, M. Woo, F. Yan-Go, and R. Harper. Heart rate responses to autonomic challenges in obstructive sleep apnea. PLoS One, Oct 2013.
[17]
J. V. Marcos, R. Hornero, D. Alvarez, F. del Campo, and C. Zamarron. Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry. Medical Engineering and Physics, 31(8):971--978, Oct 2009.
[18]
G. Matsuoka, T. Sugi, F. Kawana, and M. Nakamura. Automatic detection of apnea and eeg arousals for sleep apnea syndrome. In ICCAS-SICE, 2009.
[19]
M. Morrell, D. McRobbie, R. Quest, A. Cummin, R. Ghiassi, and D. Corfield. Changes in brain morphology associated with obstructive sleep apnea. Sleep Medicine, 4(5):451--454, Sep 2003.
[20]
C. Porth, V. Bamrah, F. Tristani, and J. Smith. The valsalva maneuver: mechanisms and clinical implications. Heart and Lung, 13(5):507--518, Sep 1984.
[21]
A. Romem, A. Romem, D. Koldobskiy, and S. M. Scharf. Diagnosis of obstructive sleep apnea using pulse oximeter derived photoplethysmographic signals. Journal of Clinical Sleep Medicine, 10(3):285--290, 2014.
[22]
N. Selvaraj and R. Narasimhan. Detection of sleep apnea on a per-second basis using respiratory signals. In IEEE Engineering in Medicine and Biology Society, 2013.
[23]
M. Suetsugi, Y. Mizuki, K. Yamamoto, S. Uchida, and Y. Watanabe. The effect of placebo administration on the first-night effect in healthy young volunteers. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 31(4):839--847, 2007.
[24]
S. Zallek, R. Redenius, H. Fisk, C. Murphy, and E. O'Neill. A single question as a sleepiness screening tool. Journal of Clinical Sleep Medicine, 4(2):143--148, Apr 2008.

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  • (2024)Effect of Weather Conditions and Transactions Records on Work Accidents in the Retail Sector – A Case StudyOptimization, Learning Algorithms and Applications10.1007/978-3-031-53025-8_3(34-48)Online publication date: 1-Feb-2024
  • (2018)OSA-weigher: an automated computational framework for identifying obstructive sleep apnea based on event phase segmentationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-018-0787-2Online publication date: 9-Apr-2018
  • (2017)APSEN: Pre-screening Tool for Sleep Apnea in a Home EnvironmentDigital Human Modeling. Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety10.1007/978-3-319-58466-9_4(36-51)Online publication date: 14-May-2017
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cover image ACM Other conferences
PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments
July 2015
526 pages
ISBN:9781450334525
DOI:10.1145/2769493
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

  • NSF: National Science Foundation
  • University of Texas at Austin: University of Texas at Austin
  • Univ. of Piraeus: University of Piraeus
  • NCRS: Demokritos National Center for Scientific Research
  • Ionian: Ionian University, GREECE

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2015

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Author Tags

  1. application
  2. classification
  3. evaluation
  4. non-intrusive
  5. obstructive sleep apnea
  6. outcome prediction
  7. screening

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PETRA '15
Sponsor:
  • NSF
  • University of Texas at Austin
  • Univ. of Piraeus
  • NCRS
  • Ionian

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View all
  • (2024)Effect of Weather Conditions and Transactions Records on Work Accidents in the Retail Sector – A Case StudyOptimization, Learning Algorithms and Applications10.1007/978-3-031-53025-8_3(34-48)Online publication date: 1-Feb-2024
  • (2018)OSA-weigher: an automated computational framework for identifying obstructive sleep apnea based on event phase segmentationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-018-0787-2Online publication date: 9-Apr-2018
  • (2017)APSEN: Pre-screening Tool for Sleep Apnea in a Home EnvironmentDigital Human Modeling. Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety10.1007/978-3-319-58466-9_4(36-51)Online publication date: 14-May-2017
  • (2016)A daytime obstructive sleep apnea severity assessment framework2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC.2016.7591205(2365-2369)Online publication date: Aug-2016

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