Quantifying cardio-respiratory phase synchronization—a comparison of five methods using ECGs of post-infarction patients. (27th April 2017)

• Record Type: Journal Article
• Authors: Kuhnhold, Anja; Schumann, Aicko Y; Bartsch, Ronny P; Ubrich, Romy; Barthel, Petra; Schmidt, Georg; Kantelhardt, Jan W
• Is Part Of: Physiological measurement. Volume 38:Number 5(2017:May)
• Page Start: 925
• Page End: 939
• Publication Date: 2017-04-27
• Subjects: phase synchronization -- cardio-respiratory coupling -- circadian rhythm; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/aa5dd3
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective: Phase synchronization between two weakly coupled oscillators occurs in many natural systems. Since it is difficult to unambiguously detect such synchronization in experimental data, several methods have been proposed for this purpose. Five popular approaches are systematically optimized and compared here. Approach: We study and apply the automated synchrogram method, the reduced synchrogram method, two variants of a gradient method, and the Fourier mode method, analyzing 24h data records from 1455 post-infarction patients, the same data with artificial inaccuracies, and corresponding surrogate data generated by Fourier phase randomization. Main results: We find that the automated synchrogram method is the most robust of all studied approaches when applied to records with missing data or artifacts, whereas the gradient methods should be preferred for noisy data and low-accuracy R-peak positions. We also show that a strong circadian rhythm occurs with much more frequent phase synchronization episodes observed during night time than during day time by all five methods. Significance: In specific applications, the identified characteristic differences as well as strengths and weaknesses of each method in detecting episodes of cardio-respiratory phase synchronization will be useful for selecting an appropriate method with respect to the type of systematic and dynamical noise in the data.