Ambulatory screening tool for sleep apnea: analyzing a single-lead electrocardiogram signal (ECG)
- 556 Downloads
The goal was to determine the utility and accuracy of automated analysis of single-lead electrocardiogram (ECG) data using two algorithms, cardiopulmonary coupling (CPC), and cyclic variation of heart rate (CVHR) to identify sleep apnea (SA).
The CPC-CVHR algorithms were applied to identify SA by analyzing ECG from diagnostic polysomnography (PSG) from 47 subjects. The studies were rescored according to updated AASM scoring rules, both manually by a certified technologist and using an FDA-approved automated scoring software, Somnolyzer (Philips Inc., Monroeville, PA). The CPC+CVHR output of Sleep Quality Index (SQI), Sleep Apnea Indicator (SAI), elevated low frequency coupling broadband (eLFCBB) and elevated low frequency coupling narrow-band (eLFCNB) were compared to the manual and automated scoring of apnea hypopnea index (AHI).
A high degree of agreement was noted between the CPC-CVHR against both the manually rescored AHI and the computerized scored AHI to identify patients with moderate and severe sleep apnea (AHI > 15). The combined CPC+CVHR algorithms, when compared to the manually scored PSG output presents sensitivity 89%, specificity 79%, agreement 85%, PPV (positive predictive value) 0.86 and NPV (negative predictive value) 0.83, and substantial Kappa 0.70. Comparing the output of the automated scoring software to the manual scoring demonstrated sensitivity 93%, specificity 79%, agreement 87%, PPV 0.87, NPV 0.88, and substantial Kappa 0.74.
The CPC+CVHR technology performed as accurately as the automated scoring software to identify patients with moderate to severe SA, demonstrating a clinically powerful tool that can be implemented in various clinical settings to identify patients at risk for SA.
KeywordsApnea hypopnea index Cardiopulmonary coupling Cyclic variation of heart rate Sleep apnea
The authors wish to thank Dr.Teofilo L. Lee-Chiong Jr., for facilitating and supervising the data collection at the Sleep Medicine Laboratory, National Jewish Health Hospital, Denver, CO and for reviewing this manuscript. We also thank Dr. John Harrington, who collected the data, clinically diagnosed patients, and reviewed all PSG studies and original scoring, and Mr. Troy Pridgeon, RPSGT for rescoring the PSG studies according to the updated AASM scoring guidelines 2017.
Solveig Magnusdottir: Initial drafting, final approval of the manuscript, guarantor of the overall content.
Hugi Hilmisson: Data analysis and final approval of the manuscript.
No funding was received for this research.
Compliance with ethical standards
The study protocol was approved by the Institutional Review Board of National Jewish Health, Denver, CO, USA. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Conflict of interest
Magnusdottir Solveig, MD. MBA: Works as an medical director of MyCardio LLC.
Has a partial ownership. SleepImage is the brand name of MyCardio LLC, a privately held entity. MyCardio LLC is a licensee of the CPC+CVHR algorithms, a method to use ECG to phenotype sleep and sleep apnea, from the Beth Israel Deaconess Medical Center, Boston, MA, USA.
Hilmisson Hugi, MA: Works as a Data Analyst for MyCardio LLC.
SleepImage is the brand name of MyCardio LLC, a privately held entity. MyCardio LLC is a licensee of the CPC+CVHR algorithms, a method to use ECG to phenotype sleep and sleep apnea, from the Beth Israel Deaconess Medical Center, Boston, MA, USA.
Informed consent was obtained from all individual participants included in the study.
Clinical trial registration number
NCT01234077 - https://clinicaltrials.gov/show/NCT01234077
Clinical trial registry name
Comparing In-laboratory Polysomnography Electrocardiogram (PSG, ECG) to Simultaneously Recorded In-laboratory ECG on the CPC M1 Device.
- 1.Frost & Sullivan (2016) Hidden health crisis costing America billions. American Academy of Sleep Medicine. (http://www.aasmnet.org/Resources/pdf/sleep-apnea-economic-crisis.pdf). Assessed 30 March 2017
- 10.Thomas RJ (2016) Cardiopulmonary coupling sleep spectrograms. In: Kryger MH, Roth T, Dement WC (eds) Principles and practice of sleep medicine, 6rd edn. Elsevier, Inc., Philadelphia, pp 1615–1623Google Scholar
- 11.Thomas RJ, Mietus JE, Peng CK, Guo D, Montgomery-Downs H, Gottlieb DJ, Wang CY, Goldberger AL (2014) Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram. Possible implications for assessing the effectiveness of sleep. Sleep Med 15(1):125–131. https://doi.org/10.1016/j.sleep.2013.10.002 CrossRefPubMedGoogle Scholar
- 12.Mietus JE, Peng CK, Ivanov PCh, Goldberger AL (2000) Detection of obstructive sleep apnea from cardiac interbeat interval time series. Comput Cardiol 27:753–6. https://doi.org/10.1109/CIC.2000.898634
- 14.Centers for Medicare & Medicaid Services (2009) Decision memo for sleep testing for obstructive sleep apnea (OSA). Assessed 30 March 2017. https://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=227&ver=11&NcaName=Sleep+Testing+for+Obstructive+Sleep+Apnea+(OSA)&CoverageSelection=National&KeyWord=sleep+testing&KeyWordLookUp=Title&KeyWordSearchType=And&bc=gAAAACAAEAAA&
- 19.Iber C, Ancoli-Israel S, Chesson AL Jr et al (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, 1st edn. American Academy of Sleep Medicine, WestchesterGoogle Scholar
- 22.Lee WH, Ahn JC, We J, Rhee CS, Lee CH, Yun PY, Yoon IY, Kim JW Cardiopulmonary coupling analysis: changes before and after treatment with mandibular advancement device. Sleep Breath 18(4):891–896. https://doi.org/10.1007/s11325-014-0961-5
- 24.Lee WH, Hong SN, Kim HJ, Rhee CS, Lee CH, Yoon IY, Kim JW (2016) A Comparison of different success definitions in non-continuous positive airway pressure treatment for obstructive sleep apnea using cardiopulmonary coupling. J Clin Sleep Med 1:35–41. https://doi.org/10.5664/jcsm.5388 CrossRefGoogle Scholar
- 27.Schramm PJ, Thomas R, Feige B, Spiegelhalder K, Riemann D (2013) Quantitative measurement of sleep quality using cardiopulmonary coupling analysis: a retrospective comparison of individuals with and without primary insomnia. Sleep Breath 17(2):713–721. https://doi.org/10.1007/s11325-012-0747-6 CrossRefPubMedGoogle Scholar
- 28.Schramm PJ, Zobel I, Monch K, Schramm E, Michalak J (2016) Sleep quality changes in chronically depressed patients treated with mindfulness-based cognitive therapy or the cognitive behavioral analysis system of Psycotherapy: a pilot study. Sleep Med 17:57–63. https://doi.org/10.1016/j.sleep.2015.09.022 CrossRefPubMedGoogle Scholar
- 29.StataCorp (2011) Stata statistical software: release 12. StataCorp LP, College StationGoogle Scholar
- 31.Corthout J, Van Huffel S, Mendez MO, Bianchi AM, Penzel T, Cerutti S (2008) Automatic screening of obstructive sleep apnea from ECG based on empirical mode decomposition and wavelet analysis. Conf Proc IEEE Eng Med Biol Soc 2008:3608–3611. https://doi.org/10.1109/IEMBS.2008.4649987 PubMedGoogle Scholar
- 36.Walker JM, Farney RJ, Rhondeau SM et al (2007) Chronic opioid use is a risk factor for the development of central sleep apnea and ataxic breathing. J Clin Sleep Med: JFCS: Off Publ Am Acad Sleep Med 3:455–461Google Scholar
- 39.Alshaer H, Bradley TD (2016) Positional sleep apnea is a cause of inter-night variability in the apnea hypopnea index. Am J Respir Crit Care Med 193: Meeting Abstracts:A2537Google Scholar