Lauren Samy
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.
- Chronic respiratory diseases. http://www.who.int/gard/publications/chronic_respiratory_diseases.pdf.Google Scholar
- Obstructive sleep apnea. http://www.aasmnet.org/resources/factsheets/sleepapnea.pdf, 2008.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- G. Matsuoka, T. Sugi, F. Kawana, and M. Nakamura. Automatic detection of apnea and eeg arousals for sleep apnea syndrome. In ICCAS-SICE, 2009.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
Index Terms
- An automated framework for predicting obstructive sleep apnea using a brief, daytime, non-intrusive test procedure
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