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Detecting synchronization between the signals from multivariate and univariate biological data. Mikhail Prokhorov.

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slide1

Detecting synchronization between the signals from multivariate and univariate biological data

Mikhail Prokhorov

Saratov Department of the Institute of Radio Engineering & Electronics of Russian Academy of Sciences

co-authors:

Vladimir Ponomarenko Saratov State University, Russia

Vladimir Gridnev Institute of Cardiology, Saratov, Russia

slide2

Methods aimed at comparing the degree of complexity of different time series. Usually the methods quantify the degree of regularity of a time series by evaluating the appearance of repetitive patterns. However, there is no straightforward correspondence between regularity, which can be measured by entropy-based algorithms, and complexity.

Complexity is associated with “meaningful structural richness” [1], which, in contrast to the outputs of random phenomena, exhibits relatively higher regularity.

[1] Information Dynamics, edited by H. Atmanspacher and H. Scheingraber, Plenum, New York, 1991.

slide3

New quantitative measurements of complexity and regularity.

Method of multiscale entropy analysis[2].

[2] M. Costa, A. L. Goldberger, C.-K. Peng, Phys. Rev. E, 2005, V.71, 021906.

It has been applied to coding and noncoding DNA sequences. It has been shown in [2] that the noncoding sequences are more complex than the coding sequences. This result supports studies suggesting, contrary to the “junk DNA” theory, that noncoding sequences contain important biological information.

slide4

[2] M. Costa,

A. L. Goldberger, C.-K. Peng,

Phys. Rev. E,

2005, V.71, 021906.

slide5

Concept of synchronization

The concept of synchronization is used to reveal interaction between two or more systems from experimental data.

The case of phase synchronization of the systems or processes, where only the phase locking is important, while no restriction on the amplitudes is imposed.

Thus, the phase synchronization of coupled systems is defined as the appearance of certain relation between their phases, while the amplitudes can remain non-correlated.

slide6

1) One of the ways for introducing phases is to store all time moments tk when the signal x(t) crosses some threshold level B in one direction (for example, from above to below) and then attribute to each such crossing a phase increase of 2.

x(t)

B

t

Within the interval between these time moments the phase f of the signal x(t) is linearly increasing as follows:

slide7

2) Another method is to reconstruct the phase portrait from a signal, project it onto the phase plane

and introduce phase as a phase angle on this plane:

slide8

3) The third way to define the phase is to construct the analytic signal [3, 4], which is a complex function of time defined as

where A(t) and (t) are respectively the instantaneous amplitude and the instantaneous phase of the signal x(t) and the function is the Hilbert transform of x(t),

[3] D. Gabor, J. IEE London, 1946, V.93, P.429–457

[4] A. Pikovsky, M. Rosenblum, J. Kurths

Synchronization: A Universal Concept in Nonlinear Science, Cambridge University Press, Cambridge, 2001.

slide9

To detect synchronization between two signals we calculate the phase difference

where 1 and 2 are the phases of the first and the second signals, n and m are integers, and is the generalized phase difference, or relative phase.

The presence of n:m phase synchronization is defined by the condition

where C is a constant. In this case the relative phase difference fluctuates around a constant value.

slide10

(a)

(b)

Fig. 3. (a) Phase synchronization 1:1 between x and y.

(b) Absence of phase synchronization.

slide11

Phase synchronization in noisy systems can be understood in a statistical sense as the appearance of a peak in the distribution of the cyclic relative phase

(b)

(a)

Fig. 4. (a) Phase synchronization in a statistical sense .

(b) Absence of phase synchronization.

slide12

Another technique widely used for the detection of synchronization between two signals is based on the analysis of the ratio of instantaneous frequencies of these signals.

The instantaneous frequency can be calculated as the rate of the instantaneous phase change.

In the region of frequency synchronization the ratio of frequencies of noisy signals remains approximately constant.

slide13

The presence of synchronization between two signals can be demonstrated by plotting a synchrogram.

To construct a synchrogram we determine the phase 2 of the slow signal at times tj when the cyclic phase of the fast signal attains a certain fixed value , , and plot versus tj, where

and m is a number of adjacent cycles of the slow signal.

In the case of n:m synchronization, attains only n different values within m adjacent cycles of the slow signal, and the synchrogram consists of n horizontal lines.

slide14

To characterize the degree of synchronization between signals various synchronization measures have been proposed.

Analyzing the relative phases we calculate the phase synchronization index

where brackets denote average over time.

By construction, if the phases are not synchronized at all and when the phase difference is constant (perfect synchronization).

slide15

The concept of synchronization is widely used for the analysis of a variety of biological data.

It has been successfully applied to human posture control data of healthy subjects and neurological patients, to multichannel magnetoencephalography data and records of muscle activity of a Parkinsonian patient, to human cardiorespiratory data and many other physiological signals.

The presence or absence of synchronization can reflect healthy dynamics.

main rhythmic processes in the human cardiovascular system
Main rhythmic processes in the human cardiovascular system
  • Main heart rhythm with a frequency of about 1 Hz
  • Respiration whose frequency is usually around 0.25 Hz
  • Process of blood pressure and heart rate regulation having in humans the fundamental frequency close to 0.1 Hz (Mayer wave)
slide17

At first we examine synchronization between the main rhythms using for the analysis multivariate data, i.e., the simultaneously measured signals of electrocardiogram, respiration and blood pressure.

slide18

Measurements and data processing

  • Subjects:healthy young volunteers
  • Recorded signals:ECG, respiration and blood pressure
  • (with the sampling frequency 250 Hz and 16-bit resolution)
  • Regimes of breathing:
  • spontaneous respiration (10 minutes)
  • fixed-frequency breathing at 0.25 Hz (10 minutes)
  • fixed-frequency breathing at 0.2 Hz (10 minutes)
  • respiration with linearly increasing frequency from 0.05 Hz to 0.3 Hz (30 minutes)
slide20

Fig. 11. Generalized phase difference (a) and the instantaneous frequency ratio (b) of the signals of ECG and spontaneous respiration, demonstrating 1:4 synchronization when 4 heartbeats occur within one respiratory cycle.

slide21

Phase synchronization between the main heart rhythm and respiration has been demonstrated by several groups of investigators [5–7].

[5] C. Schäfer, M.G. Rosenblum, J. Kurths, H.-H. Abel, Nature, 1998, V.392, P.239–240.

[6] M. Bračič-Lotrič, A. Stefanovska, Physica A, 2000, V.283, P.451–461.

[7] S. Rzeczinski, N. B. Janson, A. G. Balanov, P. V. E. McClintock, Phys. Rev. E, 2002, V.66, 051909.

We also observed phase synchronization between the main heart rhythm and respiration lasting 30 s or longer for each of the subject studied.

The duration of the longest epoch of synchronization within a 10-minute record has been about 2 minutes. Almost all subjects demonstrated the presence of several different n:m epochs of synchronization within one record.

slide22

2:1 synchronization, when two adjacent respiratory cycles contain one cycle of blood pressure slow regulation,

5:2 synchronization

[8] M.D. Prokhorov, V.I. Ponomarenko, V.I. Gridnev, M.B. Bodrov, A.B. Bespyatov, Phys. Rev. E, 2003, V.68, 041913.

Fig. 12. Generalized phase differences (a) and (b) and the instantaneous frequency ratio (c) of the signal with basic frequency fv 0.1 Hz and the signal of spontaneous respiration with average frequency fr0.25 Hz for one of the subjects.

case of paced respiration with a fixed frequency
Case of paced respiration with a fixed frequency

For the cases of breathing with the fixed frequency of 0.25 Hz and 0.2 Hz we obtain the results coinciding qualitatively with those obtained for the case of spontaneous respiration.

In comparison with the case of spontaneous respiration the case of fixed-frequency breathing is characterized by longer epochs of phase locking and higher index of phase synchronization.

Probably, it is explained by the fact that the variability of fixed-frequency respiration is several times smaller than the variability of spontaneous respiration.

case of paced respiration with linearly increasing frequency
Case of paced respiration with linearly increasing frequency

The signals were recorded continuously during 30 minutes under respiratory frequency increasing linearly from 0.05 Hz to 0.3 Hz.

Fig. 13. Generalized phase difference of the signals of ECG and respiration under paced respiration with linearly increasing frequency.

slide25

Synchronization between the process whose basic frequency is 0.1 Hz and respiration

Fig. 14. Dependence of the frequency of the process of blood pressure slow regulation fv on the frequency of respiratory frfor one of the subjects.

slide26

Fig. 15. Generalized phase difference (a), phase synchronization index (b), and the instantaneous frequency ratio (c) of the process of blood pressure regulation and respiration for one of the subjects under linearly increasing frequency of respiration fr. (d) Synchrogram, demonstrating one-band structure (1:1 synchronization) and two-band structure (2:1 synchronization).

slide27

Synchronization is not a coincidence of phases or frequencies of the rhythmic processes. Two uncoupled periodic processes always have a constant frequency ratio, but they are not synchronized.

Synchronization is an adjustment of rhythms due to interaction.

The presence of epochs where the instantaneous frequency ratio of nonstationary signals remains stable while the frequencies themselves vary, and the existence of several different n:m epochs within one record count in favor of the conclusion that the observed phenomena are associated with the process of adjustment of rhythms of interacting systems.

detecting synchronization from univariate data
Detecting synchronization from univariate data

Owing to interaction, the main rhythms of cardiovascular system appear in various signals: ECG, blood pressure, blood flow and heart rate variability (HRV).

Studying synchronization in the cardiovascular system from univariate data it is favorable to choose for the latter the sequence of R-R intervals containing information about different oscillating processes governing the cardiovascular dynamics.

The sequence of R-R intervals is the series of the time intervals Ti between the two successive R peaks

slide29

Fig. 17. Typical R-R intervals (a) and their Fourier power spectra (b).

(c) Fourier power spectra of ECG.

fh is the frequency of the main heart rhythm, fr is the respiratory frequency, and fv is the frequency of the process of slow regulation of blood pressure and heart rate.

slide30

To calculate the phase of the main heart rhythm from the sequence of R-R intervals we assume that at the time moments tk corresponding to the appearance of R peak the heartbeat phase fh is increased by 2p and within the interval between these time moments the phase fh is linearly increasing. As the result, the instantaneous phase of the main heart rhythm is determined as

slide31

To extract the instantaneous phases and frequencies of respiration and the process of slow regulation of blood pressure from the sequence of R-R intervals transformed to uniformly time spaced data we apply the three different methods using

1) bandpass filtration and the Hilbert transform

2) empirical mode decomposition and the Hilbert transform

3) wavelet transform

The obtained values were compared with the values of instantaneous phases and frequencies calculated directly from the signals of respiration and blood pressure.

bandpass filtration and the hilbert transform
Bandpass filtration and the Hilbert transform

To extract the respiratory component of HRV we filter the sequence of R-R intervals with the bandpass 0.15–0.4 Hz.

Then, for the filtered signal we apply the Hilbert transform and obtain the phase . This phase we compare with the phase computed in the similar way directly from the respiratory signal filtered with the same bandpass.

slide33

Fig. 18. Generalized phase difference (a) and the instantaneous frequency ratio (b) of the respiratory component extracted from the HRV using filtration and the measured respiratory signal.

slide34

To extract the low-frequency component of HRV with the basic frequency close to 0.1 Hz we filter the sequence of R-R intervals removing the high-frequency fluctuations (>0.15 Hz) associated predominantly with respiration, and very low-frequency oscillations (<0.05 Hz).

After this bandpass filtration we calculate the phase of the signal using the Hilbert transform and compare it with the phase computed using the Hilbert transform of the blood pressure signal filtered with the same bandpass.

slide35

Fig. 19. Generalized phase difference (a) and the instantaneous frequency ratio (b) of the rhythm of low-frequency regulation of blood pressure extracted from the HRV using bandpass filtration and the blood pressure signal filtered with the same bandpass.

empirical mode decomposition and the hilbert transform
Empirical mode decomposition and the Hilbert transform

Empirical mode decomposition (EMD) is a signal processing technique, which performs decomposition of a complicated signal into the so-called intrinsic mode functions (IMFs) [9, 10], i.e., the components with well-defined frequency.

[9] Huang N.E. et al., Proc. R. Soc. Lond. A (1998).

[10] Huang N.E. et al., Proc. R. Soc. Lond. A (2003).

slide37

To decompose the signal x(t) into IMFs we use the following algorithm:

(i) Construct the upper xmax(t) and lower xmin(t) envelopes connecting via cubic spline interpolation all the maxima and minima of x(t), respectively.

(ii) Compute

(iii) Repeat steps (i) and (ii) for until the resulting signal will possess the properties that the number of extrema is equal (or differ at most by one) to the number of zero crossings, and the mean value between the upper and lower envelope is equal to zero at any point. Denote the resulting signal by h1(t), which is the first IMF.

(iv) Take the difference and repeat steps (i)–(iii) for it to obtain the second IMF h2(t).

The procedure continues until the IMF hi(t) contains fewer than two local extrema.

slide38

Using the EMD technique for the cases of both spontaneous and fixed-frequency breathing we decompose the heartbeat time series and obtain IMFs corresponding to the high-frequency (respiratory) and low-frequency (about 0.1 Hz) components of HRV.

Next, we calculate the phases ( and ) and frequencies ( and ) of these IMFs and compare them with the phases and frequencies of the corresponding IMFs extracted directly from the signals of respiration and blood pressure.

The instantaneous phases for all the signals were computed using the Hilbert transform.

slide39

Fig. 20. Distribution of the cyclic relative phase (a) and the instantaneous frequency ratio (c) (blue line) of the associated with respiration IMFs of the HRV and respiratory signal. Distribution of the cyclic relative phase (b) and the instantaneous frequency ratio (c) (red line) of the associated with 0.1 Hz process IMFs of the HRV and blood pressure signal.

The more close correspondence is observed between the respiratory signal and the HRV intrinsic mode function associated with respiration than between the low-frequency component of the blood pressure signal and corresponding IMF of the HRV.

wavelet transform
Wavelet transform

We use the continuous wavelet transform of the signal x(t):

As a complex basis function we choose the Morlet wavelet

The wavelet spectrum of the scalar signal x(t) can be represented as two surfaces of the amplitude and phase  of the wavelet transform coefficients in the three-dimensional space. The projections of these surfaces into the (a,b) plane or the (f,b) plane allow one to trace the variation of the amplitude and phase of the wavelet transform coefficients at different scales and time moments.

slide41

Fig. 21. Distribution of the amplitude of coefficients of the HRV wavelet transform in the time-frequency plane (a).

Instantaneous frequency ratio of the HRV respiratory component obtained using the wavelet transform and the measured respiratory signal (b).

Instantaneous frequency ratio of the low-frequency components of the HRV and blood pressure signals obtained using the wavelet transform (c).

The instantaneous frequencies and of the HRV components are determined as the frequencies corresponding to the maximum amplitude of coefficients within the intervals 0.15–0.35 Hz and 0.06–0.13 Hz, respectively.

slide42

We determine the instantaneous phases and as the phases (f,b) of the wavelet transform coefficients computed for the same values of f and b as the instantaneous frequencies and , respectively.

Comparing these phases with the phases r and v calculated using the wavelet transform of the respiratory and blood pressure signals we obtained the results qualitatively similar to those above presented.

slide43

The instantaneous phase and frequency of the respiratory component derived from the sequence of R-R intervals using each of the three considered methods coincide closely with the instantaneous phase and frequency of the respiratory signal itself. We observed 1:1 phase and frequency synchronization between the respiration and the HRV respiratory component for each subject under both spontaneous and fixed-frequency breathing.

The phases and frequencies of the process with fundamental frequency of about 0.1 Hz extracted from the HRV and blood pressure signals of healthy humans are also sufficiently close but demonstrate greater difference between themselves than the respiratory oscillations.

detecting synchronization between the rhythms of cardiovascular system from r r intervals
Detecting synchronization between the rhythms of cardiovascular system from R-R intervals

Fig. 22. Generalized phase differences (a) and (b) and the instantaneous frequency ratio (c) of the heartbeat and respiration for one of the subjects under spontaneous breathing.

The respiratory phase is computed using the Hilbert transform of the HRV data filtered with the bandpass 0.15–0.4 Hz.

slide45

Fig. 23. Generalized phase difference (a) and the instantaneous frequency ratio (b) of the heartbeat and respiration for a subject under fixed-frequency breathing at 0.2 Hz.

The phase is determined using the method of EMD and the Hilbert transform.

slide46

Fig. 24. Generalized phase differences (a) and (b) and the instantaneous frequency ratio (c) of the process of blood pressure regulation and respiration.

The phases and are computed using the wavelet transform of the sequence of R-R intervals.

slide47

The results of our investigation of synchronization between the rhythms of CVS obtained from the analysis of univariate data in the form of R-R intervals of healthy subjects coincide qualitatively with the results of synchronization investigation from multivariate data.

The feasibility of detecting the presence of synchronization between the rhythms in the cardiovascular system and measuring the duration of this synchronization having at the disposal only univariate data in the form of R-R intervals opens up new possibilities for applying this measure in practice. In this case it is not necessary to record simultaneously the signals of ECG, respiration and blood pressure. Instead of this one can analyze, for example, the data of Holter monitoring widely used in cardiology.

slide48

The epochs of cardiorespiratory synchronization has been found to be longer in athletes [5] than in subjects performing recreative activity only [6, 7]. The more significant distinction of duration and the presence itself of synchronization of the cardiovascular rhythms is expected between the healthy subjects and the subjects with the disfunctions of the cardiovascular system, having usually the low HRV.

[5] C. Schäfer, M.G. Rosenblum, J. Kurths, H.-H. Abel, Nature, 1998, V.392, P.239–240.

[6] M. Bračič-Lotrič, A. Stefanovska, Physica A, 2000, V.283, P.451–461.

[7] S. Rzeczinski, N. B. Janson, A. G. Balanov, P. V. E. McClintock, Phys. Rev. E, 2002, V.66, 051909.

slide49

At Saratov cardiocenter we studied 32 patients (25 men and 11 women) aged 41-80 years after acute myocardial infarction (AMI).

The ECG and blood pressure signals were simultaneously recorded twice:

1) after 3 to 5 days after AMI

2) after 3 weeks after AMI

Control group: young healthy men without any cardiac pathology (23 records).

slide50

The duration of synchronization regions in healthy subjects has been found to be in 2.5 times longer on the average than in patients after AMI.

healthy subjects

patients after AMI

first week after AMI

three weeks after AMI

The duration of synchronization regions in patients after 3 weeks after AMI increases in 1.5 times on the average in comparison with the same patients during the first week after AMI.

However, the duration of synchronization regions remains significantly lower than in healthy subjects.

conclusion

The concept of synchronization can be successfully applied to the analysis of physiological and biological data.

  • Synchronization between different rhythmic processes can be detected even from the analysis of univariate data.
  • The phases and frequencies of the rhythmic components can be extracted from the complicated signal using the methods based on bandpass filtration and the Hilbert transform, empirical mode decomposition and the Hilbert transform, and wavelet transform.
  • The main rhythmic processes in the human cardiovascular system can be synchronized with each other.
  • The presence and duration of synchronization can reflect healthy dynamics and can be used for diagnostics.
Conclusion
references
References

Prokhorov M.D., Ponomarenko V.I., Gridnev V.I., Bodrov M.B., Bespyatov A.B., Phys. Rev. E, 2003, V.68, 041913.

Bespyatov A.B., Bodrov M.B., Gridnev V.I., Ponomarenko V.I., Prokhorov M.D., Nonlin. Phen. in Compl. Syst., 2003, V.6, N.4, P.885–893.

Ponomarenko V.I., Prokhorov M.D., Bespyatov A.B., Bodrov M.B., Gridnev V.I., Chaos, Solitons & Fractals, 2005, V.23, N.4, P.1429-1438.