A robust approach for ECG-based analysis of cardiopulmonary coupling. Issue 7 (July 2016)

• Record Type: Journal Article
• Authors: Zheng, Jiewen; Wang, Weidong; Zhang, Zhengbo; Wu, Dalei; Wu, Hao; Peng, Chung-kang
• Is Part Of: Medical engineering & physics. Volume 38:Issue 7(2016:Jul.)
• Page Start: 671
• Page End: 678
• Publication Date: 2016-07
• Subjects: ECG-derived respiration -- Cardiopulmonary coupling -- Adaptive filtering -- Phase synchronization -- Ensemble empirical mode decomposition; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2016.02.015
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: We evaluated the performance of the ECG-based CPC analysis. Adaptive filtering was performed to improve the EDR signal quality. CPC estimated from conventional EDR exhibits constant and proportional biases. CPC estimated from enhanced EDR series is more reliable. Adaptive filtering can achieve robust ECG-based CPC analysis. Abstract: Deriving respiratory signal from a surface electrocardiogram (ECG) measurement has advantage of simultaneously monitoring of cardiac and respiratory activities. ECG-based cardiopulmonary coupling (CPC) analysis estimated by heart period variability and ECG-derived respiration (EDR) shows promising applications in medical field. The aim of this paper is to provide a quantitative analysis of the ECG-based CPC, and further improve its performance. Two conventional strategies were tested to obtain EDR signal: R-S wave amplitude and area of the QRS complex. An adaptive filter was utilized to extract the common component of inter-beat interval (RRI) and EDR, generating enhanced versions of EDR signal. CPC is assessed through probing the nonlinear phase interactions between RRI series and respiratory signal. Respiratory oscillations presented in both RRI series and respiratory signals were extracted by ensemble empirical mode decomposition for coupling analysis via phase synchronization index. The results demonstrated that CPC estimated from conventional EDR series exhibits constant and proportional biases, while that estimated from enhanced EDR series is more reliable. Adaptive filtering can improve the accuracy of the ECG-based CPC estimation significantly and achieve robust CPC analysis. The improved ECG-based CPC estimation may provide additional prognostic information for both sleep medicine and autonomic function analysis.