Robust Detection of Sleep Apnea from Holter ECGs

C. Maier; H. Wenz; H. Dickhaus

doi:10.3414/ME13-02-0043

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2014; 53(04): 303-307
DOI: 10.3414/ME13-02-0043

Focus Theme – Original Articles

Schattauer GmbH

Robust Detection of Sleep Apnea from Holter ECGs

Joint Assessment of Modulations in QRS Amplitude and Respiratory Myogram Interference

C. Maier

¹Institute of Medical Biometry and Informatics, Heidelberg University Hospital, Heidelberg, Germany

²Department of Medical Informatics, Heilbronn University, Heilbronn, Germany

,

H. Wenz

³Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany

,

H. Dickhaus

¹Institute of Medical Biometry and Informatics, Heidelberg University Hospital, Heidelberg, Germany

› Institutsangaben

Abstract

Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.

Objectives: Detect presence of sleep-related breathing disorders (SRBD) in epochs of 1 min by signal analysis of Holter ECG recordings.

Methods: In 121 patients, 140 synchronized polysomnograms (PSGs) and 8-channel Holter ECGs were recorded. The only excluded condition was persistent arrhythmias. Respiratory events as scored from the PSGs were mapped to a 1 min grid and served as reference for ECG-based detection. Moreover, 69/70 recordings of the Physionet Sleep Apnea ECG Database (PADB) were included. We performed receiver operating characteristics analysis of a single, novel time-domain feature, the joint local similarity index (jLSI). Based on cross-correlation, the jLSI quantifies the time-locked occurrence of characteristic low-frequency (LF) modulations in ECG respiratory myogram interference (RMI), QRS amplitude (QRSA) and heart rate.

Results: Joint oscillations in QRSA, RMI and the envelope of RMI identified positive epochs with a sensitivity of 0.855 (PADB: 0.873) and a specificity of 0.86 (PADB: 0.88). Inclusion of heart rate did not improve detection accuracy.

Conclusions: Joint occurrence of LF-modulations in QRSA and RMI is a characteristic feature of SRBD that is robustly quantified by the jLSI and permits reliable and reproducible detection of sleep apnea in very heterogeneous settings.

Keywords

ECG - ECG-derived respiration - heart rate - respiratory myogram interference - sleep related breathing disorders

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Referenzen

References
1 Young T, Finn L, Peppard PE, Szklo-Coxe M, Austin D, Nieto FJ. et al Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 2008; 31 (08) 1071-1078.
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 Penzel T, McNames J, Murray A, de Chazal P, Moody G, Raymond B. Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med Biol Eng Comput 2002; 40 (04) 402-407.
4 de Chazal P, Heneghan C, Sheridan E, Reilly R, Nolan P, O’Malley M. Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans Biomed Eng 2003; 50 (06) 686-696. Available from http://dx.doi.org/10.1109/TBME.2003.812203
5 de Chazal P, Heneghan C, McNicholas WT. Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals. Philos Transact A Math Phys Eng Sci 2009; 367 1887 369-389. Available from http://dx.doi.org/10.1098/rsta.2008.0156
6 Mendez MO, Bianchi AM, Matteucci M, Cerutti S, Penzel T. Sleep apnea screening by autoregressive models from a single ECG lead. IEEE Trans Biomed Eng 2009; 56 (12) 2838-2850. Avail- able from http://dx.doi.org/10.1109/TBME.2009.2029563
7 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. Available from http://dx.doi.org/10.1088/0967-3334/32/11/S07
8 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; 1 8369 126-131.
9 Bystricky W, Safer A. Identification of individual sleep apnea events from the ECG using neural networks and a dynamic Markovian state model. In: Proc. Computers in Cardiology. 2004: 297-308.
10 Dickhaus H, Maier C. Detection of sleep apnea episodes from multi-lead ECGs considering different physiological influences. Methods Inf Med 2007; 46 (02) 216-221.
11 Penzel T, Moody GB, Mark RG, Goldberger AL, Peter JH. The apnea-ECG database. In: Proc. Computers in Cardiology. 2000: 2000255-258.
12 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. Circ Arrhythm Electrophysiol 2011; 4 (01) 64-72. Available from http://dx.doi.org/10.1161/CIRCEP.110.958009
13 Heneghan C, de Chazal P, Ryan S, Chua CP, Doherty L, Boyle P. et al Electrocardiogram recording as a screening tool for sleep disordered breathing. J Clin Sleep Med 2008; 4 (03) 223-228.
14 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 (04) 473-481. Available from http://dx.doi.org/10.1007/s10916-009-9383-5
15 Maier C. Beitrag zur Detektion schlafbezogener Atmungsstörungen durch Signalanalyse des Elektrokardiogramms. Heidelberg University. 2011