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Evolution of cardiorespiratory interactions with age.

Iatsenko D, Bernjak A, Stankovski T, Shiogai Y, Owen-Lynch PJ, Clarkson PB, McClintock PV, Stefanovska A - Philos Trans A Math Phys Eng Sci (2013)

Bottom Line: We describe an analysis of cardiac and respiratory time series recorded from 189 subjects of both genders aged 16-90.By application of the synchrosqueezed wavelet transform, we extract the respiratory and cardiac frequencies and phases with better time resolution than is possible with the marked events procedure.We show that the direct and indirect respiratory modulations of the heart rate both decrease with age, and that the cardiorespiratory coupling becomes less stable and more time-variable.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Lancaster University, Lancaster LA1 4YB, UK.

ABSTRACT
We describe an analysis of cardiac and respiratory time series recorded from 189 subjects of both genders aged 16-90. By application of the synchrosqueezed wavelet transform, we extract the respiratory and cardiac frequencies and phases with better time resolution than is possible with the marked events procedure. By treating the heart and respiration as coupled oscillators, we then apply a method based on Bayesian inference to find the underlying coupling parameters and their time dependence, deriving from them measures such as synchronization, coupling directionality and the relative contributions of different mechanisms. We report a detailed analysis of the reconstructed cardiorespiratory coupling function, its time evolution and age dependence. We show that the direct and indirect respiratory modulations of the heart rate both decrease with age, and that the cardiorespiratory coupling becomes less stable and more time-variable.

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Related in: MedlinePlus

Synchrosqueezed wavelet transforms of (a) the ECG and (b) respiration. The support of the main curve, i.e. the selected region, from which phase and frequency were extracted, is shown by the grey lines. The oscillations in the ECG curve are at the respiration frequency and correspond to respiratory sinus arrhythmia. (Online version in colour.)
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RSTA20110622F1: Synchrosqueezed wavelet transforms of (a) the ECG and (b) respiration. The support of the main curve, i.e. the selected region, from which phase and frequency were extracted, is shown by the grey lines. The oscillations in the ECG curve are at the respiration frequency and correspond to respiratory sinus arrhythmia. (Online version in colour.)

Mentions: The main difficulty lies in automatically tracking the support of the main curve. At time t, we define it to be the region of width nv/2 having the maximum functional , with denoting its central bin:2.3where . Here, is the central frequency of the current window, ωprev is the frequency (as in (2.2)) of the previous window (the corresponding term is used to suppress jumps) and is the mean frequency, extracted as in (2.2), but over the whole range of ω (the corresponding term is used to give more stability to the main curve). The Gaussian term is needed to give more weight to the central part of the window, i.e. if there are two or more peaks in one window, to select the support of one of them. Despite its complexity, the method provides for good flexibility and the possibility of accurate tuning. Based on the characteristic variations of the fundamental frequencies, the optimal parameters were determined to be λ=5,κ=0 for ECG and λ=10,κ=10 for respiration. Figure 1 shows examples of the extracted supports of the SWT curves corresponding to the ECG and respiration first harmonics.Figure 1.


Evolution of cardiorespiratory interactions with age.

Iatsenko D, Bernjak A, Stankovski T, Shiogai Y, Owen-Lynch PJ, Clarkson PB, McClintock PV, Stefanovska A - Philos Trans A Math Phys Eng Sci (2013)

Synchrosqueezed wavelet transforms of (a) the ECG and (b) respiration. The support of the main curve, i.e. the selected region, from which phase and frequency were extracted, is shown by the grey lines. The oscillations in the ECG curve are at the respiration frequency and correspond to respiratory sinus arrhythmia. (Online version in colour.)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4042892&req=5

RSTA20110622F1: Synchrosqueezed wavelet transforms of (a) the ECG and (b) respiration. The support of the main curve, i.e. the selected region, from which phase and frequency were extracted, is shown by the grey lines. The oscillations in the ECG curve are at the respiration frequency and correspond to respiratory sinus arrhythmia. (Online version in colour.)
Mentions: The main difficulty lies in automatically tracking the support of the main curve. At time t, we define it to be the region of width nv/2 having the maximum functional , with denoting its central bin:2.3where . Here, is the central frequency of the current window, ωprev is the frequency (as in (2.2)) of the previous window (the corresponding term is used to suppress jumps) and is the mean frequency, extracted as in (2.2), but over the whole range of ω (the corresponding term is used to give more stability to the main curve). The Gaussian term is needed to give more weight to the central part of the window, i.e. if there are two or more peaks in one window, to select the support of one of them. Despite its complexity, the method provides for good flexibility and the possibility of accurate tuning. Based on the characteristic variations of the fundamental frequencies, the optimal parameters were determined to be λ=5,κ=0 for ECG and λ=10,κ=10 for respiration. Figure 1 shows examples of the extracted supports of the SWT curves corresponding to the ECG and respiration first harmonics.Figure 1.

Bottom Line: We describe an analysis of cardiac and respiratory time series recorded from 189 subjects of both genders aged 16-90.By application of the synchrosqueezed wavelet transform, we extract the respiratory and cardiac frequencies and phases with better time resolution than is possible with the marked events procedure.We show that the direct and indirect respiratory modulations of the heart rate both decrease with age, and that the cardiorespiratory coupling becomes less stable and more time-variable.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Lancaster University, Lancaster LA1 4YB, UK.

ABSTRACT
We describe an analysis of cardiac and respiratory time series recorded from 189 subjects of both genders aged 16-90. By application of the synchrosqueezed wavelet transform, we extract the respiratory and cardiac frequencies and phases with better time resolution than is possible with the marked events procedure. By treating the heart and respiration as coupled oscillators, we then apply a method based on Bayesian inference to find the underlying coupling parameters and their time dependence, deriving from them measures such as synchronization, coupling directionality and the relative contributions of different mechanisms. We report a detailed analysis of the reconstructed cardiorespiratory coupling function, its time evolution and age dependence. We show that the direct and indirect respiratory modulations of the heart rate both decrease with age, and that the cardiorespiratory coupling becomes less stable and more time-variable.

Show MeSH
Related in: MedlinePlus