Confounding Factors in ECG-based Detection of Sleep-disordered BreathingData collection in this study has been supported by a grant of the internal young investigator program at Heidelberg University Hospital.
25 July 2017
accepted: 03 November 2017
02 May 2018 (online)
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.
- 1 Guilleminault C, Connolly S, Winkle R, Melvin K, Tilkian A. Cyclical variation of the heart rate in sleep apnoea syndrome. Mechanisms, and usefulness of 24 h electrocardiography as a screening technique. Lancet 1984; 01 (8369): 126-131.
- 2 Moody GB, Mark RG, Zoccola A, Mantero S. Derivation of Respiratory Signals from Multi-lead ECGs. In: Murray A. editor. Computers in Cardiology. vol. 12. Washington, DC: IEEE Computer Society Press; 1985: 113-116.
- 3 Maier C, Wenz H, Dickhaus H. Steps toward subject-specific classification in ECG-based detection of sleep apnea. Physiol Meas 2011; 32 (11) 1807-1819.
- 4 Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29-36.
- 5 Lado MJ, Vila XA, Rodríguez-Liñares L, Méndez AJ, Olivieri DN, Félix P. Detecting Sleep Apnea by Heart Rate Variability Analysis: Assessing the Validity of Databases and Algorithms. J Med Syst 2011; 35: 473-481.
- 6 Hayano J, Watanabe E, Saito Y, Sasaki F, Fujimoto K, Nomiyama T. et al. Screening for obstructive sleep apnea by cyclic variation of heart rate. Circulation: Arrhythmia and Electrophysiology 2011; 04 (01) 64-72.
- 7 Punjabi NM, Bandeen-Roche K, Marx JJ, Neubauer DN, Smith PL, Schwartz AR. The association between daytime sleepiness and sleep-disordered breathing in NREM and REM sleep. Sleep 2002; 25 (03) 307-314.
- 8 Penzel T, Kantelhardt JW, Lo CC, Voigt K, Vogelmeier C. Dynamics of heart rate and sleep stages in normals and patients with sleep apnea. Neuropsychopharmacology 2003; 28 (Suppl. 1): S48-S53.
- 9 Maier C, Wenz H, Dickhaus H. Robust detection of sleep apnea from Holter ECGs. Joint assessment of modulations in QRS amplitude and respiratory myogram interference. Methods Inf Med 2014; 53: 303-307.
- 10 Maier C, Dickhaus H. Central sleep apnea detection from ECG-derived respiratory signals. Application of multivariate recurrence plot analysis. Methods Inf Med 2010; 49 (05) 462-466.
- 11 Xiao M, Yan H, Song J, Yang Y, Yang X. Sleep stages classification based on heart rate variability and random forest. Biomedical Signal Processing and Control 2013; 08 (06) 624-633.
- 12 Maier C, Benz A, Dickhaus H. Segmentation of nocturnal Holter-ECG recordings with respect to different recumbent body positions. In: Cardiovascular Oscillations (ESGCO), 2014 8th Conference of the European Study Group on. IEEE 2014; 159-160.