Wavelet-Based System Identification of Short-Term Dynamic Characteristics of Arterial Baroreflex

Abstract

The assessment of arterial baroreflex function in cardiovascular diseases requires quantitative evaluation of dynamic and static baroreflex properties because of the frequent modulation of baroreflex properties with unstable hemodynamics. The purpose of this study was to identify the dynamic baroreflex properties from transient changes of step pressure inputs with background noise during a shortduration baroreflex test in anesthetized rabbits with isolated carotid sinuses, using a modified wavelet-based timefrequency analysis. The proposed analysis was able to identify the transfer function of baroreflex as well as static properties from the transient input-output responses under normal [gain at 0.04 Hz from carotid sinus pressure (CSP) to arterial pressure (n = 8); 0.29 ± 0.05 at low (40-60 mmHg), 1.28 ± 0.12 at middle (80-100 mmHg), and 0.38 ± 0.07 at high (120-140 mmHg) CSP changes] and pathophysiological [gain in control vs. phenylbiguanide (n = 8); 0.32 ± 0.07 vs. 0.39 ± 0.09 at low, 1.39 ± 0.15 vs. 0.59 ± 0.09 (p < 0.01) at middle, and 0.35 ± 0.04 vs. 0.15 ± 0.02 (p < 0.01) at high CSP changes] conditions. Subsequently, we tested the proposed wavelet-based method under closed-loop baroreflex responses; the simulation study indicates that it may be applicable to clinical situations for accurate assessment of dynamic baroreflex function. In conclusion, the dynamic baroreflex property to various pressure inputs could be simultaneously extracted from the step responses with background noise.

Time–frequency analysis of the baroreflex control of renal sympathetic nerve activity in the rat

“…A complex-valued wavelet is needed to compute directly the transfer function. In addition, this wavelet provides the best theoretical time-frequency resolution (Mallat, 1999), and was used in previous studies on the arterial baroreceptor reflex (Kashihara et al, 2009).…”

“…The value ω 0 determines the effective number of oscillation cycles in the wavelet. A value of ω 0 = 5 , which corresponds to 15 cycles in the wavelet, provides a spectral bandwidth of about 0.12f for a central frequency of f (Kashihara et al, 2009). This value has been used in further computations.…”

A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions

“…The EEG signals were convoluted by the complex Morlet wavelet (Tallon-Baudry et al, 1996; Kashihara et al, 2009) as follows: $w(t,f_0)=\exp \left(\frac{-t^2}{2{\sigma }_t^2}\right)·\exp (2\pi f_{0}it)/\sqrt{{\sigma }_t\sqrt{\pi }}.$…”

“…To consider approaches to the use of BMI/BCIs with EEG signals, an essential requirement may be the abstraction of brain activity at functional frequencies under reduced artificial noise (Tallon-Baudry et al, 1996). Such issues are effectively addressed by time–frequency analyses (Kashihara et al, 2009), which could also elucidate the neural activities that are crucial for face processing. The time–frequency data for an SVM classifier enable the efficient extraction of meaningful changes in ERP responses.…”

Cross Time-Frequency Analysis for Combining Information of Several Sources: Application to Estimation of Spontaneous Respiratory Rate from Photoplethysmography

“…Coherence estimators based on nonparametric methods have the advantage of not requiring any assumption on the time-frequency (TF) structure of the signals, and they are relatively easy to estimate. TF nonparametric methods are based on multitaper spectrogram [4, 5], wavelet transform [6, 7], empirical mode decomposition [8, 9], and quadratic TF distributions (QTFD) [10, 11]. QTFD provides TF representations of the signal power spectra and spectral coherence with fine joint TF resolution.…”