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Sleep apnea detection: accuracy of using automated ECG analysis compared to manually scored polysomnography (apnea hypopnea index)

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

Introduction

Adequate sleep is fundamental to wellness and recovery from illnesses and lack thereof is associated with disease onset and progression resulting in adverse health outcomes. Measuring sleep quality and sleep apnea (SA) at the point of care utilizing data that is already collected is feasible and cost effective, using validated methods to unlock sleep information embedded in the data. The objective of this study is to determine the utility of automated analysis of a stored, robust signal widely collected in hospital and outpatient settings, a single lead electrocardiogram (ECG), using clinically validated algorithms, cardiopulmonary coupling (CPC), to objectively and accurately identify SA.

Methods

Retrospective analysis of de-identified PSG data with expert level scoring of Apnea Hypopnea Index (AHI) dividing the cohort into severe OSA (AHI > 30), moderate (AHI 15–30), mild (AHI 5–15), and no disease (AHI < 5) was compared with automated CPC analysis of a single lead ECG collected during sleep for each subject. Statistical analysis was used to compare the two methods.

Results

Sixty-eight ECG recordings were analyzed. CPC identified patients with moderate to severe SA with sensitivity of 100%, specificity of 81%, and agreement of 93%, LR+ (positive likelihood ratio) 5.20, LR− (negative likelihood ratio) 0.00 and kappa 0.85 compared with manual scoring of AHI.

Conclusion

The automated CPC analysis of stored single lead ECG data often collected during sleep in the clinical setting can accurately identify sleep apnea, providing medically actionable information that can aid clinical decisions.

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Abbreviations

AASM:

American Academy of Sleep Medicine

AHI:

Apnea Hypopnea Index

CAP:

Cyclic alternating pattern

CPC:

Cardiopulmonary coupling

CPAP:

Continuous positive airway pressure

CVD:

Cardiovascular disease

CVHR:

Cyclic variation of heart rate

EDR:

ECG-derived respiration

ECG:

Electrocardiogram

EEG:

Electroencephalogram

eLFCBB :

Elevated low-frequency broad-band

eLFCNB :

Elevated low-frequency narrow-band

HRV:

Heart rate variability

HFC:

High-frequency coupling

LFC:

Low-frequency coupling

NREM:

Non-rapid eye movement sleep

non-CAP:

Non-cyclic alternating pattern

PABAK:

Prevalence adjusted and bias adjusted kappa

REM:

Rapid eye movement

SAI:

Sleep Apnea Indicator

SQI:

Sleep Quality Index

VLFC:

Very low frequency coupling

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Acknowledgements

Dr. Thomas Penzel of Philipps-University, Marburg, Germany who provided the data for this analysis, PhysioBank Database https://www.physionet.org/physiobank/database/apnea-ecg/(assessed November 2016).

Author information

Authors and Affiliations

  1. MyCardio-LLC, SleepImage®, 370 Interlocken Blvd., Broomfield, CO, 80021, USA

    Hugi Hilmisson

  2. Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Health at Denver-Anschutz Medical Campus, Denver, CO, 80045, USA

    Neale Lange

  3. Critical Care Pulmonary and Sleep Associates, 274 Union Blvd, Suite #110, Lakewood, CO, 80228, USA

    Stephen P. Duntley

Authors
  1. Hugi Hilmisson
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  2. Neale Lange
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  3. Stephen P. Duntley
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Contributions

Neale Lange: Drafting and final approval of the manuscript.

Hugi Hilmisson: Analysis of data, initial drafting, and final approval of the manuscript.

Stephen P Duntley: Final approval of the manuscript.

Corresponding author

Correspondence to Hugi Hilmisson.

Ethics declarations

Formal consent was not required for this analysis, as the data had already been de-identified.

Conflict of interest

Lange, Neale, MD:

Assistant Clinical Professor of Medicine, University of Colorado Health Sciences Center, Denver, CO; Partner Critical Care Pulmonary and Sleep Associates.

Dr. Lange declares no conflict of interest.

Hilmisson, Hugi, MA works as a data Analyst, MyCardio LLC. SleepImage is the brand name of MyCardio LLC, a privately held entity. MyCardio LLC is a licensee of the CPC algorithm, a method using ECG recordings during sleep to phenotype sleep and sleep apnea, from the Beth Israel Deaconess Medical Center, Boston, MA, USA.

Stephen P Duntley, MD declares no conflict of interest.

Additional information

Data registry

The data is obtained from an open access research database https://physionet.org/physiobank/database/apnoea-ecg/ contributed by Dr. Thomas Penzel of Philipps-University, Marburg, Germany.

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Hilmisson, H., Lange, N. & Duntley, S.P. Sleep apnea detection: accuracy of using automated ECG analysis compared to manually scored polysomnography (apnea hypopnea index). Sleep Breath 23, 125–133 (2019). https://doi.org/10.1007/s11325-018-1672-0

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