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Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

Purpose

Despite the increasing number of research studies of cardiopulmonary coupling (CPC) analysis, an electrocardiogram-based technique, the use of CPC in underserved population remains underexplored. This study aimed to first evaluate the reliability of CPC analysis for the detection of obstructive sleep apnea (OSA) by comparing with polysomnography (PSG)-derived sleep outcomes.

Methods

Two hundred five PSG data (149 males, age 46.8 ± 12.8 years) were used for the evaluation of CPC regarding the detection of OSA. Automated CPC analyses were based on ECG signals only. Respiratory event index (REI) derived from CPC and apnea–hypopnea index (AHI) derived from PSG were compared for agreement tests.

Results

CPC-REI positively correlated with PSG-AHI (r = 0.851, p < 0.001). After adjusting for age and gender, CPC-REI and PSG-AHI were still significantly correlated (r = 0.840, p < 0.001). The overall results of sensitivity and specificity of CPC-REI were good.

Conclusion

Compared with the gold standard PSG, CPC approach yielded acceptable results among OSA patients. ECG recording can be used for the screening or diagnosis of OSA in the general population.

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Abbreviations

AASM:

American Academy of Sleep Medicine

AHI:

apnea–hypopnea index

AUC:

area under the curve

CPC:

cardiopulmonary coupling

e-LFC:

elevated low-frequency coupling

ECG:

electrocardiogram

EEG:

electroencephalography

FDA:

Food and Drug Administration

HFC:

high-frequency coupling

HRV:

heart-rate variability

IRB:

Institutional Review Board

LFC:

low-frequency coupling

LR:

likelihood ratio

NPV:

negative predictive value

OCST:

out of center sleep testing

OSA:

obstructive sleep apnea

PPV:

positive predictive value

PSG:

polysomnography

REI:

respiratory event index

ROC:

receiver operating characteristic

RPSGT:

registered polysomnographic technologists

SD:

standard deviation

SDB:

sleep-disordered breathing

TIB:

time in bed

TST:

total sleep time

VLFC:

very-low-frequency coupling

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Funding

Yan Ma would like to acknowledge the grant support from the National Institutes of Health of the USA (T32AT000051). Yulin Wei would like to acknowledge the grant support from the National Natural Science Foundation of China (81273820).

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Authors and Affiliations

  1. Center for Dynamical Biomarkers, Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA

    Yan Ma, Dan Guo, Arron Runzhou Liu & Chung-Kang Peng

  2. Department of Otolaryngology and South Campus Sleep Center, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China

    Shuchen Sun

  3. Nanjing Integrated Traditional Chinese and Western Medicine Hospital, Nanjing, 210000, China

    Ming Zhang

  4. China-Japan Friendship Hospital, Beijing, 100029, China

    Yulin Wei

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  1. Yan Ma
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  2. Shuchen Sun
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  3. Ming Zhang
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  4. Dan Guo
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Corresponding authors

Correspondence to Yan Ma or Shuchen Sun.

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Conflict of interest

Chung-Kang Peng is a co-patent holder for the ECG-based analytic technique for phenotyping sleep and sleep apnea, known as cardiopulmonary coupling (CPC) analysis. He also receives royalties from a license issued by Beth Israel Deaconess Medical Center to MyCardio, LLC. The other authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Ethical approval and informed consent

The datasets included in this study were collected previously from clinical studies with separate protocols approved by different Institutional Review Boards (IRB) accordingly, and all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the studies. All the data we used in this secondary analysis study were de-identified. Therefore, additional IRB approval was waived.

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Comment

With the caveats of limitations, cardiopulmonary coupling patterns may be a useful approach to screen for apnea in high risk populations. However, the addition of oximetry to the assessment would be complementary - thus those with mildly hypoxic disease will be detected, while those with severe hypoxia can be risk stratified.

Robert Thomas

MA, USA

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Ma, Y., Sun, S., Zhang, M. et al. Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis. Sleep Breath 24, 231–240 (2020). https://doi.org/10.1007/s11325-019-01874-8

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