Autoregressive analysis of aortic input impedance: comparison with Fourier transform

Kubôta, Takashi; Itaya, Ryoichi; Alexander, Joe; Todaka, Koji; Sugimachi, Masaru; Sunagawa, K.

doi:10.1152/ajpheart.1991.260.3.h998

Cited by 7 publications

(2 citation statements)

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“…In addition, the multichannel autoregressive process models reciprocal influences of the first variable on the second one and vice versa by summing weighted past values from the alternate time series. It is particularly suitable for the analysis of closed‐loop interactions between linear systems under stationary conditions (Kubota et al 1991; Patton et al 1996; Nakata et al 1998). The squared coherence, transfer function gain and phase shift between RR and SP were calculated after Fourier transformation of the multivariate autoregressive coefficients.…”

Section: Methodsmentioning

confidence: 99%

Baroreflex and oscillation of heart period at 0.1 Hz studied by α‐blockade and cross‐spectral analysis in healthy humans

Cevese

Gulli

Polati

et al. 2001

The Journal of Physiology

139

137

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Spontaneous oscillations of heart period and arterial pressure can be analysed by mathematical tools to extract the main frequency-related information. This is generally contained within three principal bands: a high-frequency (HF) band, related to the respiratory rate, a low-frequency (LF) band, centred around 0.1 Hz and a very-low-frequency (VLF) band, spanning the leftmost part of the spectrum.Pressure oscillations at the respiratory frequency are linked to rhythmic changes of intrathoracic pressure due to respiratory mechanics, while low-frequency fluctuations, often identified as Mayer's waves, are the result of efferent sympathetic activity (Polosa, 1984;Cevese et al. 1995). The interpretation of heart period fluctuations is more problematic, because it has to account for the interplay of sympathetic and vagal Baroreflex and oscillation of heart period at 0.1 Hz studied by a-blockade and cross-spectral analysis in healthy humansAntonio Cevese, Giosuè Gulli, Enrico Polati *, Leonardo Gottin * and Renato Grasso † 1. Parameters derived from frequency-domain analysis of heart period and blood pressure variability are gaining increasing importance in clinical practice. However, the underlying physiological mechanisms in human subjects are not fully understood. Here we address the question as to whether the low frequency variability (›0.1 Hz) of the heart period may depend on a baroreflex-mediated response to blood pressure oscillations, induced by the a-sympathetic drive on the peripheral resistance.2. Heart period (ECG), finger arterial pressure (Finapres) and respiratory airflow were recorded in eight healthy volunteers in the supine position with metronome respiration at 0.25 Hz. We inhibited the vascular response to the sympathetic vasomotor activity with a peripheral a-blocker (urapidil) and maintained mean blood pressure at control levels with angiotensin II.3. We performed spectral and cross-spectral analysis of heart period (RR) and systolic pressure to quantify the power of low-and high-frequency oscillations, phase shift, coherence and transfer function gain.4. In control conditions, spectral analysis yielded typical results. In the low-frequency range, cross-spectral analysis showed high coherence (> 0.5) and a negative phase shift (_65.1 ± 18 deg) between RR and systolic pressure, which indicates a 1-2 s lag in heart period changes in relation to pressure. In the high-frequency region, the phase shift was close to zero, indicating simultaneous fluctuations of RR and systolic pressure. During urapidil + angiotensin II infusion the low-frequency oscillations of both blood pressure and heart period were abolished in five cases. In the remaining three cases they were substantially reduced and lost their typical cross-spectral characteristics.5. We conclude that in supine rest conditions, the oscillation of RR at low frequency is almost entirely accounted for by a baroreflex mechanism, since it is not produced in the absence of a 0.1 Hz pressure oscillation.6. The results provide physiological support fo...

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Section: Methodsmentioning

confidence: 99%

Baroreflex and oscillation of heart period at 0.1 Hz studied by α‐blockade and cross‐spectral analysis in healthy humans

Cevese

Gulli

Polati

et al. 2001

The Journal of Physiology

139

137

View full text Add to dashboard Cite

show abstract

“…Time-domain analysis. The time-domain technique of bivariate autoregression has been used also to estimate a transfer function (22). Bivariate autoregressions (20) can be fit to the original time series by using the Yule-Walker equations, which define the relationships between the covariance functions, the crosscovariance function, and the autoregression matrices (18,41).…”

Section: Applied Transfer Function Analysismentioning

confidence: 99%

An improved statistical methodology to estimate and analyze impedances and transfer functions

Curran‐Everett

Zhang

Jones

et al. 1997

Journal of Applied Physiology

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Estimating the mathematical relationship between pulsatile time series (e.g., pressure and flow) is an effective technique for studying dynamic systems. The frequency-domain relationship between time series, often calculated as an impedance (pressure/flow), is known more generally as a frequency-response or transfer function (output/input). Current statistical methods for transfer function analysis 1) assume erroneously that repeated observations on a subject are independent, 2) have limited statistical value and power, or 3) are restricted to use in single subjects rather than in an entire sample. This paper develops a regression model for transfer function analysis that corrects each of these deficiencies. Spectral densities of the input and output time series and the cross-spectral density between them are first estimated from discrete Fourier transforms and then used to obtain regression estimates of the transfer function. Statistical comparisons of the transfer function estimates use a test statistic that is distributed as chi2. Confidence intervals for amplitude and phase can also be calculated. By correctly modeling repeated observations on each subject, this improved statistical approach to transfer function estimation and analysis permits the simultaneous analysis of data from all subjects in a sample, improves the power of the transfer function model, and has broad relevance to the study of dynamic physiological systems.

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Dynamic Characteristics of the Pulmonary Circulation

AlexanderJr.¹,

Kawaguchi²,

Sunagawa³

1997

Cardiac-Vascular Remodeling and Functional Interaction

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Autoregressive analysis of aortic input impedance: comparison with Fourier transform

Cited by 7 publications

References 0 publications

Baroreflex and oscillation of heart period at 0.1 Hz studied by α‐blockade and cross‐spectral analysis in healthy humans

Baroreflex and oscillation of heart period at 0.1 Hz studied by α‐blockade and cross‐spectral analysis in healthy humans

An improved statistical methodology to estimate and analyze impedances and transfer functions

Dynamic Characteristics of the Pulmonary Circulation

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