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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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(a,b) The raw ECG and respiration signals with their peaks marked by dots. (c–f) The phases and frequencies of the cardiac (ϕh,fh) and respiratory (ϕr,fr) components, extracted from the signals by different methods. In (c,d), the phases extracted by the SWT are compared with those extracted by the Hilbert transform; the phases extracted by the ME method, apart from a constant phase shift in the ECG case, are almost indistinguishable from phases extracted by SWT within the resolution of the figure, and therefore are not shown. In (e,f), the frequencies extracted by SWT are compared with those found by the ME method; the frequency calculated from the Hilbert phase is usually inaccurate and seldom used. In both our cases, it deviates considerably from the SWT and ME frequencies.
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RSTA20110622F2: (a,b) The raw ECG and respiration signals with their peaks marked by dots. (c–f) The phases and frequencies of the cardiac (ϕh,fh) and respiratory (ϕr,fr) components, extracted from the signals by different methods. In (c,d), the phases extracted by the SWT are compared with those extracted by the Hilbert transform; the phases extracted by the ME method, apart from a constant phase shift in the ECG case, are almost indistinguishable from phases extracted by SWT within the resolution of the figure, and therefore are not shown. In (e,f), the frequencies extracted by SWT are compared with those found by the ME method; the frequency calculated from the Hilbert phase is usually inaccurate and seldom used. In both our cases, it deviates considerably from the SWT and ME frequencies.

Mentions: Figure 2 shows typical phase and frequency results extracted by this method in comparison with the values extracted by other methods. The advantage over Hilbert phase extraction is clearly evident in figure 2c,d; in figure 2c, we illustrate the well-known fact that Hilbert phase extraction is inapplicable to the ECG, whose phase is usually obtained using the ME method. For clarity, the ME phase is not shown, but the difference is clearly visible in figure 2e,f, where the frequencies extracted by SWT and ME are compared. It should be noted, however, that one might apply the Hilbert transform to the signal after bandpass filtering in the region where the first harmonic resides, and also obtain a well-behaved phase; but the frequency calculated from it will not, in practice, be accurate. Moreover, when there are additional frequency components present, or where the main frequency varies by more than a factor of two (as is often the case for respiration), the frequency range of the first harmonic will inevitably contain additional components or harmonics. By contrast, SWT phase extraction uses a narrow but time-dependent adaptive frequency range, picking up only the main curve and thus avoiding the above problems.Figure 2.


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)

(a,b) The raw ECG and respiration signals with their peaks marked by dots. (c–f) The phases and frequencies of the cardiac (ϕh,fh) and respiratory (ϕr,fr) components, extracted from the signals by different methods. In (c,d), the phases extracted by the SWT are compared with those extracted by the Hilbert transform; the phases extracted by the ME method, apart from a constant phase shift in the ECG case, are almost indistinguishable from phases extracted by SWT within the resolution of the figure, and therefore are not shown. In (e,f), the frequencies extracted by SWT are compared with those found by the ME method; the frequency calculated from the Hilbert phase is usually inaccurate and seldom used. In both our cases, it deviates considerably from the SWT and ME frequencies.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20110622F2: (a,b) The raw ECG and respiration signals with their peaks marked by dots. (c–f) The phases and frequencies of the cardiac (ϕh,fh) and respiratory (ϕr,fr) components, extracted from the signals by different methods. In (c,d), the phases extracted by the SWT are compared with those extracted by the Hilbert transform; the phases extracted by the ME method, apart from a constant phase shift in the ECG case, are almost indistinguishable from phases extracted by SWT within the resolution of the figure, and therefore are not shown. In (e,f), the frequencies extracted by SWT are compared with those found by the ME method; the frequency calculated from the Hilbert phase is usually inaccurate and seldom used. In both our cases, it deviates considerably from the SWT and ME frequencies.
Mentions: Figure 2 shows typical phase and frequency results extracted by this method in comparison with the values extracted by other methods. The advantage over Hilbert phase extraction is clearly evident in figure 2c,d; in figure 2c, we illustrate the well-known fact that Hilbert phase extraction is inapplicable to the ECG, whose phase is usually obtained using the ME method. For clarity, the ME phase is not shown, but the difference is clearly visible in figure 2e,f, where the frequencies extracted by SWT and ME are compared. It should be noted, however, that one might apply the Hilbert transform to the signal after bandpass filtering in the region where the first harmonic resides, and also obtain a well-behaved phase; but the frequency calculated from it will not, in practice, be accurate. Moreover, when there are additional frequency components present, or where the main frequency varies by more than a factor of two (as is often the case for respiration), the frequency range of the first harmonic will inevitably contain additional components or harmonics. By contrast, SWT phase extraction uses a narrow but time-dependent adaptive frequency range, picking up only the main curve and thus avoiding the above problems.Figure 2.

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