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

Mainardi, Luca

doi:10.1098/rsta.2008.0188

Cited by 85 publications

(55 citation statements)

References 55 publications

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“…The physiological and mathematical implications of both methods are widely discussed in the literature (Achten & Jeukendrup, 2003;Mainardi, 2009;Rajendra Acharya, Paul Joseph, Kannathal, Lim, & Suri, 2006;Ziegler, Piolot, Strassburger, Lambeck, & Dannehl, 1999). We would like to emphasize here that differences between methods of HRV analysis may influence the final results.…”

Section: Sdnnmentioning

confidence: 96%

Heart rate variability as a measure of cardiac autonomic function in anorexia nervosa: A review of the literature

Mazurak

Enck

Muth

et al. 2010

Euro Eating Disorders Rev

102

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Twenty papers on HRV in patients with anorexia were identified and analysed, revealing three distinct positions regarding changes of autonomic nervous system (ANS) functions in patients with AN. The majority of papers identified parasympathetic/sympathetic imbalance with parasympathetic dominance and decreased sympathetic modulation; others could not replicate these findings, but instead described sympathetic dominance; finally a group of papers could not identify any autonomic differences in comparison to control samples. We conclude that in its current state of analysis HRV may not be suitable for routine assessment of ANS function in AN patients but rather remains a research tool.

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Section: Sdnnmentioning

confidence: 96%

Heart rate variability as a measure of cardiac autonomic function in anorexia nervosa: A review of the literature

Mazurak

Enck

Muth

et al. 2010

Euro Eating Disorders Rev

102

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“…HRV dynamics have been addressed in many studies and several time-varying methods have been proposed for estimating changes in HRV spectra; for a good review on these methods and their properties see Chan et al 10 and Mainardi. 25 These methods include short time Fourier transform (STFT) and wavelet transform, 22,33,43 time-frequency distributions such as the Wigner distribution, 26,29,34,42 and time-varying autoregressive (AR) modeling based methods. 7,8,37 In addition, one fairly recent approach to model the dynamics of HRV is the point process model, 4 which has been applied e.g., for dynamic estimation of RSA component and baroreflex sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability Dynamics During Low-Dose Propofol and Dexmedetomidine Anesthesia

et al. 2012

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Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia.

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“…The data sampled from a 24-hour Holter ECG can be expressed as HRV using a time domain measure: standard deviation of normal-to-normal sinus RR intervals during a 24-hour period (SDNN), and two frequency domain analysis estimates respiratory-dependent low-frequency (LF) and high-frequency (HF) power through spectral analysis. Low-frequency power (LF: 0.04 -0.15 Hz) was analyzed as an index of sympathetic and parasympathetic nervous system activity, and high-frequency power (HF: 0.15 -0.4 Hz) was analyzed as an index of parasympathetic nervous system activity [7,12]. SDNN can be used to analyze HRV in as long a range as 24 hours.…”

Section: Baroreflex Functionmentioning

confidence: 99%

The Effect of Baroreflex Function on Blood Pressure Variability

Wei¹,

Fang²,

Ren³

et al. 2013

IJCM

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Objective: The aim of this study was to assess the relationship of blood pressure variability (BPV) and heart rate variability (HRV) to investigate the effect of baroreflex function on blood pressure variability. Methods: This study consisted of 111 subjects, including 32 normotensives and 79 hypertensives. All the subjects were given two concurrent tests: 24-hour Holter ECG and ambulatory blood pressure monitoring. According to standard deviation of normal-to-normal sinus RR intervals (SDNN) derived from the Holter ECG, the hypertensives were divided into two groups: an HRV normal group with SDNN > 100 ms and an HRV abnormal group with SDNN < 100 ms. HRV analysis used the time domain measure SDNN and two frequency domain analyses using low-frequency and high-frequency power. BPV analysis involved a formula correlated to each blood pressure value. Results: BPV was significantly higher in the HRV abnormal group compared with the HRV normal group in the hypertensives (0.018 ± 0.0033 vs 0.014 ± 0.0032, P < 0.05). In the HRV abnormal group, BPV value of the older hypertensive participants was higher than the younger participants (0.019 ± 0.0024 vs 0.017 ± 0.0037, P = 0.048). BPV and HRV were correlated in the younger hypertensives (r = −0.314, P < 0.05) and older hypertensives (r = −0.692, P < 0.001). Conclusions: Baroreflex function had effect on BPV. Factors like aging could cause damage to the baroreflex sensitivity, which in turn had influence on BPV. There may be benefits in restoration of baroreflex function to reduce BPV, especially in hypertensive patients.

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

Cited by 85 publications

References 55 publications

Heart rate variability as a measure of cardiac autonomic function in anorexia nervosa: A review of the literature

Heart rate variability as a measure of cardiac autonomic function in anorexia nervosa: A review of the literature

Heart Rate Variability Dynamics During Low-Dose Propofol and Dexmedetomidine Anesthesia

The Effect of Baroreflex Function on Blood Pressure Variability

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