Methods Inf Med 2018; 57(03): 146-151
DOI: 10.3414/ME17-02-0005
Focus Theme – Original Article
Schattauer GmbH

Confounding Factors in ECG-based Detection of Sleep-disordered Breathing

Christoph Maier
1  Department of Medical Informatics, Heilbronn University, Heilbronn, Germany
,
Hartmut Dickhaus
2  Institute for Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany
› Author Affiliations
Data collection in this study has been supported by a grant of the internal young investigator program at Heidelberg University Hospital.
Further Information

Publication History

received: 25 July 2017

accepted: 03 November 2017

Publication Date:
02 May 2018 (online)

Summary

Objectives: To assess the relevance of various potential confounding factors (comorbidities, obesity, body position, ECG lead, respiratory event type and sleep stage) on the detectability of sleep-related breathing disorders from the ECG.

Methods: A set of 140 simultaneous recordings of polysomnograms and 8-channel Holter ECGs taken from 121 patients with suspected sleep related breathing disorders is stratified with respect to the named factors. Minute-by-minute apnea detection performance is assessed using separate receiver operating characteristics curves for each of the subgroups. The detection is based on parameters of heart rate, ECG amplitude and respiratory myogram interference in the ECG. We consider spectral and correlation-based features.

Results: The results show that typical comorbidities and supine body position impede apnea detection from the heart rate. Availability of multiple ECG-leads improves the robustness of ECG amplitude based detection with respect to posture influence. But quite robust apnea detection is achievable with even a single ECG channel – preferably lead I. Sleep stages and respiratory event type have a significant and quite consistent effect on apnea detection sensitivity with better results for light sleep stages, and worse results for REM sleep. Mixed and obstructive events are better detected than central apneas and hypopneas.

Conclusions: Various factors confound the detection of sleep apnea based on the ECG. These findings should be taken into account when comparing results obtained from different data sets and may help to understand limitations of current and to improve robustness of new detection algorithms.