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.
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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.
No funding was received for this research.
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.
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Cite this article
Magnusdottir, S., Hilmisson, H. Ambulatory screening tool for sleep apnea: analyzing a single-lead electrocardiogram signal (ECG). Sleep Breath 22, 421–429 (2018). https://doi.org/10.1007/s11325-017-1566-6
- Apnea hypopnea index
- Cardiopulmonary coupling
- Cyclic variation of heart rate
- Sleep apnea