Quantifying cardio-respiratory phase synchronization-a comparison of five methods using ECGs of post-infarction patients.
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.


METHODS
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.


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.


CONCLUSIONS
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.
Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany. Physique et Matériaux, Faculté des Sciences, de la Technologie et de la Communication, Université du Luxembourg, Luxembourg.
Anja
A
Kuhnhold
Aicko Y
AY
Schumann
Ronny P
RP
Bartsch
Romy
R
Ubrich
Petra
P
Barthel
Georg
G
Schmidt
Jan W
JW
Kantelhardt
D000368:Aged; D002320:Cardiovascular Physiological Phenomena; D002940:Circadian Rhythm; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D009203:Myocardial Infarction / Q000503:physiopathology*; D012143:Respiratory Physiological Phenomena; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors