Abstract

Objective. The deceleration capacity (DC) and acceleration capacity (AC) of heart rate, which are recently proposed variants to the heart rate variability, are calculated from unevenly sampled RR interval signals using phase-rectified signal averaging. Although uneven sampling of these signals compromises heart rate variability analyses, its effect on DC and AC analyses remains to be addressed. Approach. We assess preprocessing (i.e., interpolation and resampling) of RR interval signals on the diagnostic effect of DC and AC from simulation and clinical data. The simulation analysis synthesizes unevenly sampled RR interval signals with known frequency components to evaluate the preprocessing performance for frequency extraction. The clinical analysis compares the conventional DC and AC calculation with the calculation using preprocessed RR interval signals on 24-hour data acquired from normal subjects and chronic heart failure patients. Main Results. The assessment of frequency components in the RR intervals using wavelet analysis becomes more robust with preprocessing. Moreover, preprocessing improves the diagnostic ability based on DC and AC for chronic heart failure patients, with area under the receiver operating characteristic curve increasing from 0.920 to 0.942 for DC and from 0.818 to 0.923 for AC. Significance. Both the simulation and clinical analyses demonstrate that interpolation and resampling of unevenly sampled RR interval signals improve the performance of DC and AC, enabling the discrimination of CHF patients from healthy controls.

Highlights

  • Assessing the autonomic nervous system (ANS) activity is crucial for analyses such as risk prediction of mortality after myocardial infarction [1], diagnosis of chronic heart failure (CHF) [2, 3], and prediction of diabetic neuropathy [4, 5]

  • When the algorithm is applied to RR interval (RRI) signals, deceleration capacity (DC) and acceleration capacity (AC) correspond to the wavelet coefficients from the center of the phase-rectified signal averaging (PRSA) curve

  • Unevenly spaced samples may compromise heart rate variability (HRV) analysis in the frequency domain [19]. erefore, we aimed to evaluate the effect of preprocessing RRI signals on diagnosis based on DC and AC through both simulation and clinical analyses. e simulation analysis synthesizes unevenly sampled RRI signals with known frequency components, and a subsequent wavelet analysis illustrates the effect of preprocessing on the extraction of characteristic frequency components

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Summary

Introduction

Assessing the autonomic nervous system (ANS) activity is crucial for analyses such as risk prediction of mortality after myocardial infarction [1], diagnosis of chronic heart failure (CHF) [2, 3], and prediction of diabetic neuropathy [4, 5]. New indices obtained from RR interval (RRI) signals have been developed for improved characterization of the ANS activity. The deceleration capacity (DC) and acceleration capacity (AC) of the heart rate, which were proposed a Computational and Mathematical Methods in Medicine decade ago, are promising for assessing the ANS activity [10]. Ey aim to separately characterize deceleration and acceleration components in HRV, outperforming conventional HRV indices in prediction of mortality and risk stratification of postmyocardial infarction patients [11,12,13]. When the algorithm is applied to RRI signals, DC and AC correspond to the wavelet coefficients from the center of the PRSA curve. PRSA aims to eliminate noise influence by averaging phasesynchronized segments, and the wavelet transform quantifies the averaged signal. The quality of phase synchronization is a determinant factor in PRSA

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