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      Measuring Heart Rate Variability with Wavelet Thresholds and Energy Components in Healthy Subjects and Patients with Congestive Heart Failure


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          Background: Analysis of wavelet thresholds and energy components can be used to differentiate patients with congestive heart failure (CHF) from healthy subjects and to demonstrate the improvement of heart rate variability after treatment with beta-blockers in the patient group. Methods: Nine patients with CHF without concurrent beta-blocker therapy and 15 healthy volunteers were recruited and received 24-hour Holter recording. Wavelet thresholds and energy components were calculated. The patients with CHF were then treated with atenolol for 3 months. Another Holter recording was repeated and associated wavelet parameters were estimated. Results: Wavelet thresholds were statistically higher in normal subjects than those derived from patients with CHF (62.40 ± 20.65 vs. 16.90 ± 9.72, p < 0.001). The difference was still significant after control of heart rate and the total number of heart beats (74.85 ± 22.68 vs. 32.51 ± 18.73, p < 0.001). The energy component of HRV in the finest wavelet domain was also significantly higher in the normal subjects (243.01 ± 237.69 × 10<sup>–6</sup> vs. 40.65 ± 39.65 × 10<sup>–6</sup>, p = 0.001). These two measures increased after treatment with beta-blockers in the patients with CHF, but not to the extent of those of normal subjects. Conclusion: Analysis of wavelet thresholds and energy components has provided robust and accurate measurements for background HRV.

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          Most cited references 18

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          Functional assessment of heart rate variability: physiological basis and practical applications.

          The autonomic nervous system dynamically controls the response of the body to a range of external and internal stimuli, providing physiological stability in the individual. With the progress of information technology, it is now possible to explore the functioning of this system reliably and non-invasively using comprehensive and functional analysis of heart rate variability. This method is already an established tool in cardiology research, and is increasingly being used for a range of clinical applications. This review describes the theoretical basis and practical applications for this emerging technique.
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            Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches?

             Andreas Bruns (2004)
            Spectral signal analysis constitutes one of the most important and most commonly used analytical tools for the evaluation of neurophysiological signals. It is not only the spectral parameters per se (amplitude and phase) which are of interest, but there is also a variety of measures derived from them, including important coupling measures like coherence or phase synchrony. After reviewing some of these measures in order to underline the widespread relevance of spectral analysis, this report compares the three classical spectral analysis approaches: Fourier, Hilbert and wavelet transform. Recently, there seems to be increasing acceptance of the notion that Hilbert- or wavelet-based analyses be in some way superior to Fourier-based analyses. The present article counters such views by demonstrating that the three techniques are in fact formally (i.e. mathematically) equivalent when using the class of wavelets that is typically applied in spectral analyses. Moreover, spectral amplitude serves as an example to show that Fourier, Hilbert and wavelet analysis also yield equivalent results in practical applications to neuronal signals.
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              Prediction of tonic parasympathetic cardiac control using respiratory sinus arrhythmia: the need for respiratory control.

              Respiratory sinus arrhythmia (RSA) has received much attention in recent years due to the large body of evidence indicating that variations in this phenomenon represent alterations in parasympathetic cardiac control. Although it appears that respiratory sinus arrhythmia is mediated by vagal mechanisms, the extent to which the well-known respiratory influences (i.e., rate and tidal volume) on respiratory sinus arrhythmia (in altering its magnitude) may moderate the relationship between RSA and cardiac vagal tone has never been systematically studied. We addressed this issue by examining intraindividual relationships among RSA magnitude, respiration (rate and tidal volume), and heart period among six healthy male adults after intravenous administration of 10 mg propranolol, a beta-adrenergic blocker. Subjects were exposed to various behavioral tasks which altered all physiological variables measured. Variations in heart period after beta blockade were assumed to be predominantly vagally mediated. Within-subject regression analyses consistently showed that respiratory parameters influenced RSA magnitude, but not tonic variations in beta-blocked heart period, suggesting that respiratory-mediated RSA alterations are not associated with changes in cardiac vagal tone. Only when respiratory variables were statistically controlled was there evidence of a reasonable correspondence between beta-blocked heart period and RSA amplitude, providing support for the idea that respiratory parameters need to be controlled when using RSA amplitude as an index of cardiac vagal tone. Repeated-measures analyses of variance of mean levels of heart period and respiratory sinus arrhythmia across subjects supplemented and supported the intraindividual results. These findings point to the importance of controlling for respiratory parameters when using respiratory sinus arrhythmia as a cardiac vagal index.

                Author and article information

                S. Karger AG
                November 2006
                15 November 2006
                : 106
                : 4
                : 207-214
                aNational Taiwan University Hospital Yun-Lin Branch, Yun-Lin, and bNational Taiwan University Hospital, Taipei, Taiwan, ROC
                93124 Cardiology 2006;106:207–214
                © 2006 S. Karger AG, Basel

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                Page count
                Figures: 5, Tables: 1, References: 29, Pages: 8
                Original Research


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