Quantifying the PR interval pattern during dynamic exercise and recovery.

Aline Cabasson (1) , Olivier Meste (1) , Gregory Blain (1) , Stephane Bermon (2)

1 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe SIGNAL
2 IM2S

We present a novel analysis tool for time delay estimation in electrocardiographic signal processing. This tool enhances PR interval estimation (index of the atrioventricular conduction time) by limiting the distortion effect of the T wave overlapping the P wave at high heart rates. Our approach consists of modeling the T wave, cancelling its influence, and finally estimating the PR intervals during exercise and recovery with the proposed generalized Woody method. Different models of the T wave are presented and compared in a statistical summary that quantitatively justifies the improvements introduced by this study. Among the different models tested, we found that a piecewise linear function significantly reduces the T wave-induced bias in the estimation process. Combining this modeling with the proposed time delay estimation method leads to accurate PR interval estimation. Using this method on real ECGs recorded during exercise and its recovery, we found: 1) that the slopes of PR interval series in the early recovery phase are dependent on the subjects' training status (average of the slopes for sedentary men = 0.11 ms/s, and for athlete men = 0.28 ms/s), and 2) an hysteresis phenomenon exists in the relation PR/RR intervals when data from exercise and recovery are compared.

Domains: Bioengineering; Signal and Image processing

Aline Cabasson, Olivier Meste, Gregory Blain, Stephane Bermon. Quantifying the PR interval pattern during dynamic exercise and recovery.. IEEE Transactions on Biomedical Engineering, 2009, 56 (11), pp.2675-83. ⟨10.1109/TBME.2009.2028694⟩. ⟨hal-00430395⟩