Automatic Decomposition of Wigner Distribution and its Application to Heart Rate Variability

2004 ◽  
Vol 43 (01) ◽  
pp. 17-21 ◽  
Author(s):  
N. Montano ◽  
S. Cerutti ◽  
L. T. Mainardi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wigner Distribution ◽  
Early Response ◽  
Least Square ◽  
Spectral Parameters ◽  
Time Frequency ◽  
Novel Approach ◽  
Stationary Signals ◽  
Automatic Decomposition

Summary Objective: We introduce an algorithm for the automatic decomposition of Wigner Distribution (WD) and we applied it for the quantitative extraction of Heart Rate Variability (HRV) spectral parameters during non-stationary events. Early response to tilt was investigated. Methods: Quantitative analysis of multi-components non-stationary signals is obtained through an automatic decomposition of WD based on least square (LS) fitting of the instantaneous autocorrelation function (ACF). Through this approach the different signal and interference terms which contributes to the ACF may be separated and their parameters (instantaneous frequency and amplitude) quantified. A beat-to-beat monitoring of HRV spectral components is obtained. Results: Analysis of simulated signals demonstrated the capability of the proposed approach to track and separate the signal components. Analysis of HRV data evidenced different dynamics in the early Autonomic Nervous System (ANS) response to tilt. Conclusions: The novel approach to the quantification of the beat-to-beat HRV spectral parameters obtained from decomposition of Wigner distribution was demonstrated to be effective in the analysis of HRV data. Relevant physiological information about the dynamics of the early sympathetic response to tilt were obtained. The method is a general approach which may be employed for a quantitative time-frequency analysis of non-stationary biological signals.

scholarly journals Temporal contribution of body movement to very long-term heart rate variability in humans

2000 ◽  
Vol 278 (4) ◽  
pp. H1035-H1041 ◽  
Author(s):  
Naoko Aoyagi ◽  
Kyoko Ohashi ◽  
Shinji Tomono ◽  
Yoshiharu Yamamoto
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wigner Distribution ◽  
Body Movement ◽  
Ambulatory Monitoring ◽  
Coarse Graining ◽  
Single Subject ◽  
Activity Levels ◽  
Time Frequency

A newly developed, very long-term (∼7 days) ambulatory monitoring system for assessing beat-to-beat heart rate variability (HRV) and body movements (BM) was used to study the mechanism(s) responsible for the long-period oscillation in human HRV. Data continuously collected from five healthy subjects were analyzed by 1) standard auto- and cross-spectral techniques, 2) a cross-Wigner distribution (WD; a time-frequency analysis) between BM and HRV for 10-s averaged data, and 3) coarse-graining spectral analysis for 600 successive cardiac cycles. The results showed 1) a clear circadian rhythm in HRV and BM, 2) a 1/ f β-type spectrum in HRV and BM at ultradian frequencies, and 3) coherent relationships between BM and HRV only at specific ultradian as well as circadian frequencies, indicated by significant ( P < 0.05) levels of the squared coherence and temporal localizations of the covariance between BM and HRV in the cross-WD. In a single subject, an instance in which the behavioral (mean BM) and autonomic [HRV power >0.15 Hz and mean heart rate (HR)] rhythmicities were dissociated occurred when the individual had an irregular daily life. It was concluded that the long-term HRV in normal humans contained persistent oscillations synchronized with those of BM at ultradian frequencies but could not be explained exclusively by activity levels of the subjects.

On the quantification of heart rate variability spectral parameters using time–frequency and time-varying methods

2008 ◽  
Vol 367 (1887) ◽  
pp. 255-275 ◽  
Author(s):  
Luca T Mainardi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Low Frequency ◽  
Time Varying ◽  
Frequency Method ◽  
Spectral Parameters ◽  
Time Frequency ◽  
Daily Life Activities ◽  
Common Time ◽  
Reliable Quantification

In the last decades, one of the main challenges in the study of heart rate variability (HRV) signals has been the quantification of the low-frequency (LF) and high-frequency (HF) components of the HRV spectrum during non-stationary events. At this regard, different time–frequency and time-varying approaches have been proposed with the aim to track the modification of the HRV spectra during ischaemic attacks, provocative stress testing, sleep or daily-life activities. The quantitative evaluation of power (and frequencies) of the LF and HF components has been approached in various ways depending on the selected time–frequency method. This paper is an excursus through the most common time–frequency/time-varying representation of the HRV signal with a special emphasis on the algorithms employed for the reliable quantification of the LF and HF parameters and their tracking.

Effect of amiodarone on heart rate variability in patients with newly diagnosed atrial fibrillation (clinical observation)

2020 ◽  
pp. 81-85
Author(s):  
E. P. Popova ◽  
O. T. Bogova ◽  
S. N. Puzin ◽  
D. A. Sychyov ◽  
V. P. Fisenko
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Drug Therapy ◽  
Heart Function ◽  
Newly Diagnosed ◽  
Class Iii ◽  
Spectral Parameters ◽  
Patient Will ◽  
The Individual

Spectral analysis of heart rate variability gives an idea of the role of the autonomic nervous system in the regulation of chronotropic heart function. This method can be used to evaluate the effectiveness of drug therapy. Drug therapy should be carried out taking into account the individual clinical form of atrial fibrillation. Information about the vegetative status of the patient will undoubtedly increase the effectiveness of treatment. In this study, spectral parameters were studied in patients with newly diagnosed atrial fibrillation. The effect of antiarrhythmic drug class III amiodarone on the spectral parameters of heart rate variability was studied.

scholarly journals Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

2017 ◽  
Vol 123 (2) ◽  
pp. 344-351 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Renata Maria Lataro ◽  
Jaci Airton Castania ◽  
Carlos Alberto Aguiar Silva ◽  
Helio Cesar Salgado ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiscale Entropy ◽  
Time Frequency ◽  
Cardiac Control ◽  
Long Time ◽  
Nonlinear Features ◽  
Short Time

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.

Sign in / Sign up

Export Citation Format

Share Document