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Front Physiol. 2013 Apr 16;4:81. doi: 10.3389/fphys.2013.00081. eCollection 2013.

Search for HRV-parameters that detect a sympathetic shift in heart failure patients on β-blocker treatment.

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1
AEF/Systems Physiology, Heart Failure Research Center, Academic Medical Center at the University of Amsterdam Amsterdam, Netherlands.

Abstract

BACKGROUND:

A sympathetic shift in heart rate variability (HRV) from high to lower frequencies may be an early signal of deterioration in a monitored patient. Most chronic heart failure (CHF) patients receive β-blockers. This tends to obscure HRV observation by increasing the fast variations. We tested which HRV parameters would still detect the change into a sympathetic state.

METHODS AND RESULTS:

β-blocker (Carvedilol®) treated CHF patients underwent a protocol of 10 min supine rest, followed by 10 min active standing. CHF patients (NYHA Class II-IV) n = 15, 10m/5f, mean age 58.4 years (47-72); healthy controls n = 29, 18m/11f, mean age 62.9 years (49-78). Interbeat intervals (IBI) were extracted from the finger blood pressure wave (Nexfin®). Both linear and non-linear HRV analyses were applied that (1) might be able to differentiate patients from healthy controls under resting conditions and (2) detect the change into a sympathetic state in the present short recordings. Linear: mean-IBI, SD-IBI, root mean square of successive differences (rMSSD), pIBI-50 (the proportion of intervals that differs by more than 50 ms from the previous), LF, HF, and LF/HF ratio. Non-linear: Sample entropy (SampEn), Multiscale entropy (MSE), and derived: Multiscale variance (MSV) and Multiscale rMSSD (MSD). In the supine resting situation patients differed from controls by having higher HF and, consequently, lower LF/HF. In addition their longer range (τ = 6-10) MSE was lower as well. The sympathetic shift was, in controls, detected by mean-IBI, rMSSD, pIBI-50, and LF/HF, all going down; in CHF by mean-IBI, rMSSD, pIBI-50, and MSD (τ = 6-10) going down. MSD6-10 introduced here works as a band-pass filter favoring frequencies from 0.02 to 0.1 Hz.

CONCLUSIONS:

In β-blocker treated CHF patients, traditional time domain analysis (mean-IBI, rMSSD, pIBI-50) and MSD6-10 provide the most useful information to detect a condition change.

KEYWORDS:

entropy; frequency domain analysis; heart rate variability; home monitoring; intensive care; time domain analysis

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