Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health

Abstract

This paper introduces a modified technique based on Hilbert-Huang transform (HHT) to improve the spectrum estimates of heart rate variability (HRV). In order to make the beat-to-beat (RR) interval be a function of time and produce an evenly sampled time series, we first adopt a preprocessing method to interpolate and resample the original RR interval. Then, the HHT, which is based on the empirical mode decomposition (EMD) approach to decompose the HRV signal into several monocomponent signals that become analytic signals by means of Hilbert transform, is proposed to extract the features of preprocessed time series and to characterize the dynamic behaviors of parasympathetic and sympathetic nervous system of heart. At last, the frequency behaviors of the Hilbert spectrum and Hilbert marginal spectrum (HMS) are studied to estimate the spectral traits of HRV signals. In this paper, two kinds of experiment data are used to compare our method with the conventional power spectral density (PSD) estimation. The analysis results of the simulated HRV series show that interpolation and resampling are basic requirements for HRV data processing, and HMS is superior to PSD estimation. On the other hand, in order to further prove the superiority of our approach, real HRV signals are collected from seven young health subjects under the condition that autonomic nervous system (ANS) is blocked by certain acute selective blocking drugs: atropine and metoprolol. The high-frequency power/total power ratio and low-frequency power/high-frequency power ratio indicate that compared with the Fourier spectrum based on principal dynamic mode, our method is more sensitive and effective to identify the low-frequency and high-frequency bands of HRV.

Novel application of multi dynamic trend analysis as a sensitive tool for detecting the effects of aging and congestive heart failure on heart rate variability

“…Elderly healthy CHF investigating the effects of aging and CHF. 16,34,44 More specifically, the intrinsic trend fluctuations of the complex HRV analyzed using MDTA were found to be decreased in healthy elderly subjects and CHF subjects. Because the MDTA method is a nonlinear approach to HRV analysis comprising EMD and a variable-sized sliding-window method, our findings indirectly support the hypothesis that HRV is a nonlinear and nonstationary biosignal, and consequently, that a nonlinear approach is more appropriate for analyzing it.…”

Resampling of ECG signal for improved morphology alignment

“…Resampling of an ECG signal is often required in different applications such as cardiovascular engineering [2, 3] and ECG-based biometrics [4,5] to yield the same number of samples in each of the heartbeats. Generally the uniform resampling technique has been used [2,3]. This method ignores the deformation of morphology in resampled heartbeats.…”

“…Thus, in a digital electrocardiogram (ECG) signal, each of the heartbeats may contain a different number of samples. Resampling of an ECG signal is often required in different applications such as cardiovascular engineering [2,3] and ECG-based biometrics [4,5] to yield the same number of samples in each of the heartbeats. Generally the uniform resampling technique has been used [2,3].…”

“…By resampling, we obtain an equal number of samples m for each of the heartbeats. The new m samples can be obtained from an interpolating function [2,3] (e.g. cubic spline interpolation).…”

“…Uniform resampling: In the uniform resampling method [2,3], the same resampling function is applied to all segments of the ECG heartbeat signal (i.e.…”