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Conf Proc IEEE Eng Med Biol Soc. Author manuscript; available in PMC 2011 March 17.

Published in final edited form as:

PMCID: PMC3059743

NIHMSID: NIHMS247746

Address for correspondence: Sandun Kodituwakku, Applied Signal Processing Group, School of Engineering, The Australian National University, Canberra, Australia, Email: ua.ude.una@ukkawutidok.nudnas

The publisher's final edited version of this article is available at Conf Proc IEEE Eng Med Biol Soc

See other articles in PMC that cite the published article.

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heartbeat. We propose an algorithm for quantifying instantaneous RSA as applied to heart beat interval and respiratory recordings under dynamic respiration conditions. The blood volume pressure derived heart beat series (pulse intervals, PI) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PI and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated by a frequency domain transfer function approach. The model is statistically validated using Kolmogorov-Smirnov (KS) goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. Experimental results confirm the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states.

A large number of autonomic and hemodynamic parameters are influenced by respiratory activity. Among them, respiratory sinus arrhythmia (RSA) can be defined as the variations in the heart rate which occur simultaneously with the respiration cycle [1]. At typical resting respiratory frequencies, heart rate increases during inspiration, and decreases during expiration. RSA is usually considered as an indirect measure of vagal cardiac control, although at lower respiratory frequencies sympathetic cardiac control contributes to RSA as well [1]. A key problem in cardiorespiratory engineering is to efficiently and accurately quantify RSA under different physiological conditions, such as variations in respiration and changes in sympathetic-parasympathetic inputs. A solution to this issue could yield critical insights into the mechanisms involved in short-term cardiorespiratory coupling [1] [2].

In previous work [3] [4], RSA was defined using simple time domain measures of beat interval series. Filtering and transfer function approaches were also used in quantifying RSA [5] [6], and a bivariate autoregressive model was further proposed to estimate the time-varying RSA gain [7] [8]. As most of these methods are not able to overcome stationarity issues and estimate fast changes in RSA at arbitrarily small time scales, a point process framework for heart beat dynamics [9] [10] has been proposed to assess RSA within an adaptive point process filtering algorithm [11].

In this paper, we use a novel local maximum likelihood method within a point process framework to allow for instantaneous estimation of RSA. Importantly, as measures based on the traditional subdivision in oscillatory frequency components might not be reliable in the presence of non-stationary respiratory patterns, we further propose a new method for dynamically selecting the RSA gain within the transfer function spectrum, based on a time-frequency characterization of the respiratory cycle and the time-varying coherence between respiration and heart beat series (pulse intervals, PI). Such combined method is capable of computing reliable, instantaneous estimates of RSA by accounting for rapid dynamic changes in both respiration patterns and autonomic inputs. The new algorithm is validated on simulated data, as well as applied to recordings from subjects practicing meditation.

Integrate and fire models are regularly used to simulate heart beats, and such model postulates that the resulting times between two firing events (the PIs) have statistical properties of an inverse Gaussian process [9]. Additionally, autonomic inputs to the SA node are part of the cardiovascular control circuitry, thus the PI variations are dynamic or time-varying [9]. For this reason, we here model the pulse intervals as a history dependent, inverse gaussian point process model with time-varying model parameters. Assume in a given observation interval (0,T], K successive pulses are recorded: *0* < *u _{1}* <

$$f(t/{u}_{k})={\left[\frac{\theta (t)}{2\pi {(t-{u}_{k})}^{3}}\right]}^{\frac{1}{2}}exp\left\{-\frac{1}{2}\frac{\theta (t){[t-{u}_{k}-{\mu}_{PI}(t)]}^{2}}{{\mu}_{PI}^{2}(t)(t-{u}_{k})}\right\},$$

(1)

where *t* is any time satisfying *t* > *u _{k}*, and

Heart rate is often defined as the reciprocal of PIs [12]. For PIs measured in seconds, *r = c/(t* − *u _{k}),* where

$${\mu}_{HR}(t)=c\left(\frac{1}{{\mu}_{PI}(t)}+\frac{1}{\theta (t)}\right),$$

(2)

$${\sigma}_{HR}(t)=c{\left[\frac{2{\mu}_{PI}(t)+\theta (t)}{{\mu}_{PI}(t){\theta}^{2}(t)}\right]}^{\frac{1}{2}},$$

(3)

and they respectively define the instantaneous estimates of heart rate and heart rate variability.

The influence of past autonomic inputs and respiration activity on the PIs are incorporated into the model by defining a bivariate regression on the mean of the point process probability density (1),

$${\mu}_{PI}(t)={a}_{0}(t)+\sum _{k=1}^{p}{a}_{k}(t){PI}_{t-k}+\sum _{k=1}^{q}{b}_{k}(t){RP}_{t-k}.$$

(4)

Note that the respiration signal (RP) is sampled at the pulse timings, so that both respiration and PIs are synchronized.

RSA can then be defined as the transfer function from RP to PI,

$${H}_{12}(\omega ,t)=\frac{{\sum}_{k=1}^{q}{b}_{k}(t){z}^{-k}{\mid}_{z={e}^{j2\pi {f}_{s}}}}{{\sum}_{k=1}^{p}{a}_{k}(t){z}^{-k}{\mid}_{z={e}^{j2\pi {f}_{s}}}}.$$

(5)

We propose two methods of estimating the RSA gain from the above transfer function. First, the time-varying respiration spectrum *P _{RP}(ω,t)* is used to estimate the frequency

$$\underset{\omega}{max}[{P}_{RP}(\omega ,t)]={P}_{RP}({\omega}_{RP},t).$$

(6)

Then, RSA gain can be estimated by evaluating (5) at ω_{RP},

$${\mathit{RSA}}_{\mathit{gain}}^{a}(t)=\phantom{\rule{0.16667em}{0ex}}\mid {H}_{12}({\omega}_{RP},t)\mid .$$

(7)

Second, we evaluate the RSA gain at the frequency where maximum interaction between PIs and respiration occur. In this regards, we use the time-varying autoregressive coherence spectrum *Coh(ω, t)* [5] to estimate the frequency *ω _{coh}(t)* where coherence is maximum, i.e.,

$$\underset{\omega}{max}[\mathit{Coh}(\omega ,t)]=\mathit{Coh}({\omega}_{\mathit{coh}},t),$$

(8)

and the RSA gain is evaluated at *ω _{coh}*,

$${\mathit{RSA}}_{\mathit{gain}}^{b}(t)=\mid {H}_{12}({\omega}_{\mathit{coh}},t)\mid .$$

(9)

A local maximum likelihood method [14] was used to estimate the unknown time-varying parameter set *ξ = {{a _{k}}^{p}_{k=0}, {b_{k}}^{q}_{k=1}, θ}*. In estimating

$$logf({u}_{t-l:t})=\sum _{j=2}^{n}w(t-{u}_{j})logf({u}_{j}-{u}_{j-1})+w(t-{u}_{j})log\underset{t-{u}_{n}}{\overset{\infty}{\int}}f(\upsilon )d\upsilon ,$$

(10)

where *w(t* − *u _{j}) = α^{t}*

Goodness-of-fit of the proposed model was evaluated using a Kolmogorov-Smirnov (KS) test based on the time-rescaling theorem [15]. The test uses the conditional intensity function given by,

$$\lambda (t)=\frac{f(t)}{1-{\int}_{{u}_{n}}^{t}f(\upsilon )d\upsilon}$$

(11)

to transform pulse events into independent observations on the interval [0, 1], and the KS plot allows to test the agreement of the transformed observations and the ideal uniform probability density. The transformed quantiles’ autocorrelation function is further computed to check independence of the transformed intervals.

Meditation essentially consists of subjects focusing on their breath and passively ignoring everyday thoughts. For this reason, mastering meditation techniques has been often mischaracterized as “simply resting”. To such extent, the present experiment considers two groups of subjects, one group consists of experienced meditators, whereas the other group consists of subjects with no meditation experience. The experiment starts with 6 minutes of control period, followed by 1 minute of fixation, then 24 minutes of meditation, followed by 1 minute of fixation, and finally 6 minutes with the subject silently counting random numbers [16]. The control subjects are asked to simply rest during the “meditation” period. During the experiment, blood volume pressure (BVP) signal, and a belt signal proportional to lung volume changes were recorded at 1 kHz. BVP was used to identify the PIs, and the belt signal was sampled at these pulse timings to obtain the respiration values at the beats.

First, optimal values for regression orders of the bivariate model *p* and *q*, maximum-likelihood interval *l*, and weighting time constant *α* were obtained by minimizing the Akaike Information Criterion for maximum likelihood estimation, as well as the KS distance on the KS plot. This empirical optimization yields to *p = 4*, *q = 6*, *l = 90s*, and α = 0.98. The KS plot and autocorrelation function for this final model are shown in Figure 1 for a representative subject.

KS plot (left panel) and Autocorrelation function (right panel) for one experienced meditator. Dashed lines indicate the 95% confidence bounds.

Then, the proposed maximum likelihood point process model was applied to both meditating and control subjects with model parameters as described above. The resulting instantaneous mean PI estimate *μ _{PI} (t)* (4), and HRV index

Meditation subject: Instantaneous mean PI (top), and Heart rate variance (bottom) estimated by the point process model. Dashed lines (from left to right) indicate the start times of control, meditation, numbers epochs respectively, and dotted lines indicate **...**

Control subject: Instantaneous mean PI (top), and Heart rate variance (bottom) estimated by the point process model.

Average statistics for 6 subjects from each group were performed for mean PI, HRV, and the LF/HF index [12]. Results show no significant changes in all parameters in the control group, whereas a general increase in HRV was observed in the meditating group. Importantly, we observed that the respiratory cycle often tends to slow down during both meditation practice and relaxation, and some of the respiratory variability may fall well within the low frequency (LF) band of the standard HRV frequency band division [12]. As a consequence, the RSA-elicited PI variability would mirror the respiratory spectral distribution, with vagal modulated cardiac dynamics distributed between both LF and HF bands, thus potentially biasing the LF/HF index.

Instantaneous time-frequency RSA estimates are computed using the bivariate AR point process and transfer function methods described in Section II-B. The algorithms were first tested with artificial series simulating both sudden and gradual shifts in RSA gain, as well as shifts in the main frequency where the two series are coupled. Results (not shown) demonstrate that the proposed algorithm is very robust and stable even under sudden changes in the foremost oscillatory (respiratory) frequency, while still being capable of accurately estimating sudden and gradual changes in the RSA gain.

As applied to the considered experimental data, the first important finding was that both *RSA ^{a}_{gain}(t)* and

Meditation subject: (a) Frequency where coherence between PI and RP is maximum (f_{coh}), (b) Maximum coherence, (c) RSA gain estimated at f_{coh} by the point process model (nrpu = normalised respiratory units).

Control subject: (a) Frequency where coherence between PI and RP is maximum (f_{coh}), (b) Maximum coherence, (c) RSA gain estimated at f_{coh} by the point process model.

Average RSA statistics for 6 subjects from each group are shown in Table I. Results point at a major increase (58%) in RSA during meditation, with high statistical significance *(p = 0.031)* compared to the baseline epoch. On the other hand, the control group does not show any significant change in RSA during relaxation *(p > 0.2)*. During the silent random number generation phase, RSA values are statistically comparable to baseline levels for both groups.

Average RSA gain of 6 subjects each in the meditation and control groups. The percentage change, and Wilcoxon signed rank test results compared to the baseline epoch are also reported.

Differently from the LF/HF assessment, our proposed method is robust to variations in respiration patterns as we rely on the respiratory spectral power distribution and the dynamic coherence between respiration and PIs, without any artificial subdivision in frequency bands. As a further proof that RSA gains are not considerably biased by possible shifts in respiration frequency, we have computed similar indices at a fixed frequency, where coherence is the highest on average along each experiment, and verified that average statistics using these quantities confirm the presented results.

In summary, while traditional HRV indices confirm previous findings pointing at an autonomic assessment not necessarily reflective of quiescent cardiac dynamics [17], and possibly biased by altered breathing patterns [18], our statistical RSA appraisal substantiates previous reports of a marked increase of RSA during meditation [19].

We have proposed a maximum likelihood point process model for instantaneous RSA estimation combined with a time-frequency RSA evaluation based on respiration spectrum and coherence. Our novel framework allows for robust tracking of RSA changes at any time resolution, as well as overcoming potential problems associated with traditional subdivision in standard frequency bands in the presence of distinctive respiratory dynamics. The new algorithm was applied to subjects either practicing meditation, or just asked to relax under equal conditions. Results show a significant increase in RSA under meditation practice which is not evident in the control group, encouraging further investigation into the effects of meditation techniques on cardiovascular control and into the potential benefits of meditation on cardiovascular health. Overall, the dynamic statistical measures computed from our point process framework provide the basis for potential realtime indicators for ambulatory monitoring and instantaneous assessment of autonomic control in clinical practice.

This work was supported by the National Institutes of Health (NIH) under Grant R01-HL084502, Grant R01-DA015644, Grant DP1-OD003646.

^{1}Note that for a fair comparison across subjects, the RSA gain (9) was normalized by the standard deviation of the corresponding respiration signal.

1. Saul JP, Cohen RJ. Vagal control of the heart: Experimental basis and clinical implications. Futura Publishing Co. Inc; 1994. Respiratory sinus arrhythmia; pp. 511–536.

2. Eckberg DL. Human sinus arrhythmia as an index of vagal cardiac outflow. J Appl Physiol. 1983;54:961–966. [PubMed]

3. Hirsch JA, Bishop B. Respiratory sinus arrhythmia in humans: How breathing pattern modulates heart rate. Amer J Physiol. 1981;241:H620–H629. [PubMed]

4. White JA, Semple E, Norum RA. Sinus arrhythmia as an estimate of maximal aerobic power in man. J Physiol. 1990;432:41.

5. Womack BF. The analysis of respiratory sinus arrhythmia using spectral analysis and digital filtering. IEEE Trans Biomed Eng. 1971;18:399–409. [PubMed]

6. Saul JP, Berger RD, Chen MH, Cohen RJ. Transfer function analysis of autonomic regulation. II. Respiratory sinus arrhythmia. Am J Physiol - Heart and Circulatory Physiol. 1989;256(1):H153–H161. [PubMed]

7. Barbieri R, Waldmann RA, Di Virgilio V, Triedman JK, Bianchi AM, Cerutti S, Saul JP. Continuous quantification of baroreflex and respiratory control of heart rate by use of bivariate autoregressive techniques. Ann Noninvasive Electrocardiol. 1996;1:264–277.

8. Barbieri R, Bianchi AM, Triedman JK, Mainardi LT, Cerutti S, Saul JP. Model dependency of multivariate autoregressive spectral analysis. IEEE Eng Med Bio Mag. 1997;16(5):74–85. [PubMed]

9. Barbieri R, Matten EC, Alabi ARA, Brown EN. A pointprocess model of human heartbeat intervals: new definitions of heart rate and heart rate variability. Am J Physio- Heart and Circulatory Physiology. 2005;288(1):H424–H435. [PubMed]

10. Barbieri R, Brown EN. Analysis of heartbeat dynamics by point process adaptive filtering. IEEE Trans Biomed Eng. 2006;53(1):4–12. [PubMed]

11. Chen Z, Brown E, Barbieri R. Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics. IEEE Trans Biomed Eng. 2009;56(7):1791–1802. [PMC free article] [PubMed]

12. Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ. Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use. Am Heart Assoc Circulation. 1996;93(5):1043–1065.

13. Ross SM. Introduction to probability models. 9. Academic Pr; 2007.

14. Loader C. Local regression and likelihood. Springer; 1999.

15. Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM. The time-rescaling theorem and its application to neural spike train data analysis. Neural Comput. 2002;14(2):325–346. [PubMed]

16. Lazar SW, Kerr CE, Wasserman RH, Gray JR, Greve DN, Treadway MT, McGarvey M, Quinn BT, Dusek JA, Benson H, RSL, MCI, Fischl B. Meditation experience is associated with increased cortical thickness. Neuroreport. 2005;16(17):1893–1897. [PMC free article] [PubMed]

17. Peng CK, Henry IC, Mietus JE, Hausdorff JM, Khalsa G, Benson H, Goldberger AL. Heart rate dynamics during three forms of meditation. Int J Cardio. 2004;95(1):19–27. [PubMed]

18. Lehrer P, Sasaki Y, Saito Y. Zazen and cardiac variability. Psychosomatic Medicine. 1999;61(6):812–821. [PubMed]

19. Ditto B, Eclache M, Goldman N. Short-term autonomic and cardiovascular effects of mindfulness body scan meditation. Annals of Behavioral Medicine. 2006;32(3):227–234. [PubMed]

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