Non-invasive baroreflex sensitivity assessment using wavelet transfer function-based time–frequency analysis

Keissar, Kobi; Maestri, Roberto; Pinna, Gian Domenico; Rovere, Maria Teresa La; Gilad, Oded

doi:10.1088/0967-3334/31/7/011

Cited by 16 publications

(12 citation statements)

References 28 publications

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“…One of the methods that overcome this limitation is the joint time and frequency (TF) approach which allows for changes in spontaneous fluctuations in ABP and HR to be tracked in time [41]. As such, it permits the study of baroreflex as a dynamic rather than static mechanism [42], thereby expanding the range of available tests to hemodynamic maneuvers where transient changes in physiological signals cannot be assessed by standard techniques.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Baroreflex Sensitivity Using Time-Frequency Analysis during Postural Change and Hypercapnia

Kazimierska

Plaček

Uryga

et al. 2019

Computational and Mathematical Methods in Medicine

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Baroreflex is a mechanism of short-term neural control responsible for maintaining stable levels of arterial blood pressure (ABP) in an ABP-heart rate negative feedback loop. Its function is assessed by baroreflex sensitivity (BRS)—a parameter which quantifies the relationship between changes in ABP and corresponding changes in heart rate (HR). The effect of postural change as well as the effect of changes in blood O2 and CO2 have been the focus of multiple previous studies on BRS. However, little is known about the influence of the combination of these two factors on dynamic baroreflex response. Furthermore, classical methods used for BRS assessment are based on the assumption of stationarity that may lead to unreliable results in the case of mostly nonstationary cardiovascular signals. Therefore, we aimed to investigate BRS during repeated transitions between squatting and standing in normal end-tidal CO2 (EtCO2) conditions (normocapnia) and conditions of progressively increasing EtCO2 with a decreasing level of O2 (hypercapnia with hypoxia) using joint time and frequency domain (TF) approach to BRS estimation that overcomes the limitation of classical methods. Noninvasive continuous measurements of ABP and EtCO2 were conducted in a group of 40 healthy young volunteers. The time course of BRS was estimated from TF representations of pulse interval variability and systolic pressure variability, their coherence, and phase spectra. The relationship between time-variant BRS and indices of ABP and HR was analyzed during postural change in normocapnia and hypercapnia with hypoxia. In normocapnia, observed trends in all measures were in accordance with previous studies, supporting the validity of presented TF method. Similar but slightly attenuated response to postural change was observed in hypercapnia with hypoxia. Our results show the merits of the nonstationary methods as a tool to study the cardiovascular system during short-term hemodynamic changes.

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Section: Introductionmentioning

confidence: 99%

Assessment of Baroreflex Sensitivity Using Time-Frequency Analysis during Postural Change and Hypercapnia

Kazimierska

Plaček

Uryga

et al. 2019

Computational and Mathematical Methods in Medicine

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“…In the analysis of cardiovascular signals, the estimation of spectral coherence in the joint time-frequency (TF) domain has many potential fields of application. For example, the localization of TF regions in which two signals are coupled can be applied in the time-varying characterization of baroreflex [48,49,50], or in the assessment of the degree of similarity between different signals to validate the use of one signal, and its derived measures, as surrogates of original ones [51].…”

Section: Time-frequency Coherencementioning

confidence: 99%

“…Beside those described in this chapter, others have been used in biomedical applications. These include the time-scale coherence [75] based on the continuous wavelet transform, which has been proposed to study cardiovascular dynamics [76,77,50] as well as neural connectivity [63,78,79,80]. These methods are characterized by a non-homogeneous TF resolution and are suited for signals presenting high frequency components of short duration and low frequency components of long duration.…”

Section: Final Considerationsmentioning

confidence: 99%

Time-Frequency Analysis of Cardiovascular Signals and Their Dynamic Interactions

Orini

Laguna

Mainardi

et al. 2017

Complexity and Nonlinearity in Cardiovascular Signals

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Cardiovascular signals are intrinsically non-stationary and interact through dynamic mechanisms to maintain blood pressure homeostasis in response to internal and external perturbations. The assessment of changes in cardiovascular signals and in their interactions provides valuable information regarding the cardiovascular function. Time-frequency analysis is a useful tool to study the time-varying nature of the cardiovascular system because it provides a joint representation of a signal in the temporal and spectral domain that allows to track the instantaneous frequency, amplitude and phase of non-stationary processes. The time-frequency distributions described in this chapter belong to the Cohen's class, and can be derived from the Wigner-Ville distribution, which represents the fundamental basis of this unified framework. Time-frequency analysis can be extended to the study of the dynamic interactions between two or more non-stationary processes. Time-frequency coherence, phase-delay, phase-locking and partialspectra are estimators that assess changes in the coupling strength and phase shift of signals generated by a complex system. This chapter introduces the reader to multivariate time-frequency analysis and covers both theoretical and practical aspects. The application of these methodologies in the study of the dynamic interactions between the most important variables of the cardiovascular function is discussed. In the introduction, classical spectral analysis of cardiovascular signals is reviewed along with its physiological interpretation. The limitations of this framework provides a motivation for implementing non-stationary tools. In the first section, time-frequency representations based on the Wigner-Ville distribution are introduced and important aspects, such as the interference cross-terms and their elimination, the time and frequency resolution and the estimation of time-frequency spectra, are described. The second section describes algorithms to assess the dynamic interactions between non-stationary signals, including time-frequency coherence, phase delay and partial spectra, while the third section provides examples of multivariate time-frequency analysis of cardiovascular data recorded during a standard test to induce an autonomic response.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Peláez-Coca

Orini

Lázaro

et al. 2013

Computational and Mathematical Methods in Medicine

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A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration.

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Non-invasive baroreflex sensitivity assessment using wavelet transfer function-based time–frequency analysis

Cited by 16 publications

References 28 publications

Assessment of Baroreflex Sensitivity Using Time-Frequency Analysis during Postural Change and Hypercapnia

Assessment of Baroreflex Sensitivity Using Time-Frequency Analysis during Postural Change and Hypercapnia

Time-Frequency Analysis of Cardiovascular Signals and Their Dynamic Interactions

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

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