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European Journal of Heart Failure 2002 4(5):617-625; doi:10.1016/S1388-9842(02)00098-3
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© 2002 European Society of Cardiology

Cardiorespiratory system dynamics in chronic heart failure

L. Mangina,*, A. Montib and C. Médigueb

a Service de Pneumologie et Unité de Réanimation, Laboratoire de Physiopathologie Cardio-Respiratoire UPRES 2397, Pavillon Rambuteau, Groupe Hospitalier Pitié-Salpétrière, 47-83 Bd de l'Hôpital 75013, Paris, France
b Institut National de Recherche en Informatique et Automatique Le Chesnay, France

* Corresponding author. Tel.: +33-142-176-751; fax: +33-142-176-843. E-mail address: laurence.mangin{at}psl.ap-hop-paris.fr


   Abstract
Top
 Abstract
1. Introduction
 2. Methods
 3. Results
 4. Discussion
 Appendix A. Complex demodulation
 Appendix B. Pseudo Wigner-Ville...
 References
 
Aims: With the complex demodulation (CDM) method, we assessed the instantaneous amplitude and frequency of cardiovascular (CV) and respiratory oscillations, and the instant phase (IP) between the CV and respiratory signals using respiration as a periodic forced stimulation. We hypothesised a possible lack of synchronisation between CV and respiratory signals under regular breathing at different frequencies.

Methods: RR interval (ECG), blood pressure (SBP/DBP, Finapress), respiration (Respitrace) were monitored during two random-order periods of voluntary paced-breathing (0.15 Hz/0.25 Hz) in 10 moderate CHF patients and 10 age-matched controls. The CDM method provides the amplitude and frequency of a particular spectral component as a function of time in both LF and HF bands. IP between CV and respiratory oscillations was assessed using the real modulating breathing rate.

Results: (i) Continuous phase variations between CV oscillations and the respiratory signal were evidenced in CHF patients, the slower the breathing rate, the greater the phase variation (RR/Resp; 0.25 Hz, 23±17°; 0.15 Hz, 46±57°, P<0.01; RR/Resp at 0.15 Hz 6±3 vs. 46±57 P<0.01 controls vs. CHF). Phase was constant in controls. (ii) In patients, the instant amplitude of the cardiovascular oscillations in the high frequency domain is more markedly altered when the breathing rate was slowed down as compared to controls.

Conclusion: The lack of synchronisation between physiological signals during voluntary breathing in CHF patients highlights a central uncoupling between CV and respiratory neuronal activities.

Key Words: Heart failure • Respiration • Autonomic nervous system • Central nervous system • Spectral analysis

Received October 12, 2001; Revised December 5, 2001; Accepted February 22, 2002


   1. Introduction
Top
 Abstract
 1. Introduction
2. Methods
 3. Results
 4. Discussion
 Appendix A. Complex demodulation
 Appendix B. Pseudo Wigner-Ville...
 References
 
Autonomic nervous system dysfunction is a common feature in patients with chronic heart failure (CHF) [1]. Elucidation of the mechanisms underlying dysautonomia in heart failure is of crucial importance for risk stratification since abnormal indexes of autonomic activity, such as heart rate variability and baroreflex sensitivity, have been shown to predict mortality in CHF patients [2,3]. Various non-invasive signal processing techniques have been developed for analysing cardiovascular oscillations. In the frequency domain, power spectral analysis using Fast Fourier transform is the most widely used in clinical studies. However, it averages frequency and amplitude throughout the observation period and is therefore reliable only if little change occurs during this period. Many situations of physiological interest require instant analysis of cardiovascular autonomic control. Instant spectral techniques have been used recently for studying heart rate oscillations during transitions in physiological [4,5] or pathological conditions [6–8]. Continuous and instantaneous assessment of hemodynamic oscillations provide interesting data on cardiovascular dynamics. Complex demodulation measures changes over time in amplitude, frequency and phase as a function of time for oscillations in a frequency band of interest. Thus, it is well-suited to follow abrupt changes in the pattern of cardiovascular oscillations [9–13]. To gain insight into the cardiorespiratory system dynamics in chronic heart failure, we assessed the instant amplitude and frequency of cardiovascular oscillations and the instant phase between cardiovascular oscillations and the respiratory signal using respiration as a periodic forcing term. During cardiac failure, we hypothesised a possible lack of synchronisation between these physiological signals under regular breathing at different frequencies.


   2. Methods
Top
 Abstract
 1. Introduction
 2. Methods
3. Results
 4. Discussion
 Appendix A. Complex demodulation
 Appendix B. Pseudo Wigner-Ville...
 References
 
2.1. Patients and controls
We studied 10 patients with CHF (two patients class II and eight patients class III NYHA; six men and four women; mean age=48±8 years; mean ejection fraction=24±5%; peak O2 consumption=16±4 ml kg–1 min–1, body mass index=25±2 kg m–2). All were in sinus rhythm. The cause of CHF was idiopathic dilated cardiomyopathy in seven patients and ischemic heart disease in three patients. All patients had been clinically stable for at least 2 months at the time of the measurements, with no evidence of acute coronary events for at least 6 months. Five patients were receiving ACE inhibitors and diuretics, five were receiving digoxin. They remained stable with respect to therapy for at least 1 month before the study. Exclusion criteria were pulmonary disease, significant renal dysfunction, diabetes mellitus, arterial hypertension, autonomic neuropathy, and treatment with β-blockers. The age and sex-match control group included 10 non-smoking untreated healthy volunteers (six men, four women; mean age=45±5 years). All patients and controls gave their informed consent to participation in the study.

2.2. Experimental protocol
The recordings were performed in the morning, with the subjects lying in a darkened quiet room at 24 °C. During 5 min, they were educated to pace their breathing with a periodic auditory stimulus. After a 15-min rest, the recordings were performed during two, 5-min random-order periods of breathing paced at 0.25 Hz (15 breaths/min, which is close to the spontaneous breathing rate) and 0.15 Hz (9 breaths/min), respectively. All 10 patients were able to tolerate the slow breathing rate easily. During the paced-breathing periods, the subjects were allowed to control the depth and shape of each breath so as to preserve normal alveolar ventilation.

2.3. Measurements
During each 5-min paced-breathing period, we recorded heart rate (ECG in a lead producing a prominent R wave), blood pressure (photoplethysmographic transducer Finapress 2300, Ohmeda), and respiration (impedance method; Respitrace Systems positioned over the lower part of the chest). Non-invasive arterial blood pressure was assessed continuously with the finger kept at a constant level relative to the right atrium. This technique has been validated for power spectral analysis of arterial pressure variability [14]. The Finapress was allowed to autocalibrate during the recordings.

2.4. Data acquisition and processing
ECG, blood pressure and respiratory signals sampled at 500 Hz were acquired on a PC computer hard disk using specific computer software (Acknowledge III, BIOPAC Systems, USA). Signal processing was performed with LARY-CR, a physiological signal analysis software developed at the French National Institute for Research in Computer Science and Automation (INRIA) in the SCILAB-SCICOS environment. Rhythm detection was applied to the raw cardiovascular signals. QRS complex was detected by a derivative/adaptive threshold algorithm to provide a continuous series of RR intervals (tachogram). Systolic blood pressure detection (SBP) was based on the maximal systolic value of the signal detected by an adaptive threshold (systogram) and diastolic blood pressure detection on the first minimal diastolic point detected before the maximal systolic value (diastogram) [15]. These rhythms were resampled at 4 Hz to obtain equidistant time series. Using a Finite Impulse Filter, preliminary band-pass filtering was performed to increase the signal-to-noise ratio in both the low frequency (LF) (0.09±0.03 Hz) and the high frequency (HF) band (0.25±0.03 and 0.15±0.03 Hz depending on the period). The time series were used for both complex demodulation and smoothed pseudo Wigner–Ville distribution analyses.

2.5. Instantaneous amplitude and phase estimate with the complex demodulation (CDM) method
Cardiovascular oscillations were processed using a time local version of harmonic analysis, namely CDM. CDM has been used to analyse biomedical signals, including RR-interval, respiratory and arterial pressure oscillations [9,10,12,13]. CDM provides the amplitude and frequency of a particular spectral component as a function of time in both LF and HF bands. In our study, reference frequencies for cardiovascular signal analysis were 0.09 Hz for the LF band and the instantaneous breathing rate for the HF band, respectively. The instantaneous phase between cardiovascular and respiratory signals was assessed using the real modulating breathing rate (see Appendices A and B).

A time–frequency domain analysis, the smoothed pseudo Wigner–Ville distribution (SPWVD) was used to validate the results obtained by CDM. SPWVD provides a spectral profile for every beat, each depending on the preceding and subsequent events and allows time-dependent quantification of the spectral power and instantaneous central frequency (ICF) of both the LF and HF components of cardiovascular and respiratory oscillations [16] (see Appendix B). In each subject, we compared CDM and SPWVD methods for instantaneous amplitude and centre frequency (ICF) of RR interval, SBP and DBP oscillations (data not shown). As an example, Fig. 1 shows in a control subject, the SBP oscillation at 0.15 Hz breathing rate. The curves for instant amplitude and ICF obtained with the two methods are identical.


Figure 1
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  		Fig. 1 Comparison of CDM and SPWVD methods for instant HF amplitude and centre frequency (ICF) of SBP at 0.15 Hz respiratory rhythm in a control subject. It demonstrates the superposition of the curves for the SPWVD (black) and CDM (red) HF amplitude and the SPWVD (green) and CDM (blue) ICF estimates.

 
CDM is based on the assumption that the component of interest is present within the predefined frequency band. To avoid potential bias, periods with transient absence of the high- or low frequency oscillations evaluated by both CDM and SPWVD were discarded from phase and frequency analyses: indeed, cardiac failure is a disease condition characterised by a marked reduction of cardiovascular oscillations.

2.6. Statistical analysis
All results are presented as mean±S.D. Data at the two breathing rates were compared using non-parametric tests (Wilcoxon test for paired comparisons and Mann–Whitney test for impaired comparisons). P values of less than 5% were considered statistically significant.


   3. Results
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
4. Discussion
 Appendix A. Complex demodulation
 Appendix B. Pseudo Wigner-Ville...
 References
 
3.1. Controls
Respiratory frequency, RR interval and SBP at the two breathing rates are shown in Table 1. RR intervals and their standard deviations were significantly higher at slower breathing rates. High frequency oscillations of RR interval and SBP significantly increased as the breathing rate diminished (Table 2). Low frequency oscillations of RR intervals and SBP were not significantly modified by the breathing rate.


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  		Table 1 Mean values of respiratory frequency, RR interval and SBP during voluntary breathing at two frequencies

 


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  		Table 2 Instant amplitude of RR interval and SBP oscillations, phase variation between RR/SBP and respiratory signals

 
The phase variation between cardiovascular oscillations and respiration was the same at the two rates (Table 2 and Fig. 2, left).


Figure 2
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  		Fig. 2 Instant phase, amplitude and centre frequency of SBP oscillation at 0.15 Hz in control (left) and CHF patient (right) with the CDM method. During cardiac failure, a major phase variation (black curve) between SBP and respiration at 0.15 Hz respiratory rhythm is evidenced over 60 s, which means the absence of co-ordination of both physiological signals. Instantaneous HF amplitude (red curve) is significantly depressed during cardiac failure compared with normal subject. Blue curve shows the stability of the ICF.

 
3.2. Heart failure patients
Respiratory frequency, RR interval and SBP at the two breathing rates are shown in Table 1. Although SBP was slightly lower at the slower breathing rate, RR interval was not significantly modified.

High frequency oscillations of RR interval and SBP were significantly increased at 0.15 Hz as compared to 0.25 Hz (P<0.01 and P<0.05, Table 2). As compared to controls, these variables showed lesser decreases at the slower breathing rate (P<0.05, Table 2 and Fig. 3). In the low frequency domain, both the RR interval and SBP oscillations (mean amplitude) were slightly increased as the breathing rate diminished. The minimum amplitude of the RR oscillation in the low frequency domain was significantly lower at 0.15 Hz than in the controls.


Figure 3
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  		Fig. 3 Instantaneous HF amplitude of RR interval at 0.15 Hz respiratory rhythm in a CHF patient (red) and control (blue) with the CDM method. The minimal amplitude in CHF (red point) is more markedly depressed than the minimal amplitude of the control (blue point).

 
The phase variation was greater in the CHF patients than in the controls; this difference was statistically significant during the 0.15 Hz period (Table 2, Fig. 2 right).

Fig. 4 illustrates the continuous fluctuating phase between cardiovascular and respiratory signals during cardiac failure: the sum of the phase variation in normalised units (between RR, SBP, DBP and respiratory signals) per subjects is given: continuous phase variations between cardiovascular oscillations and the respiratory signal were evidenced in CHF patients, the slower the breathing rate, the greater the phase variation.


Figure 4
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  		Fig. 4 Each symbol ({diamond}) represents the sum of the phase variation between RR, SBP, DBP and respiration in normalised unit per subjects at 0.25 (left) and 0.15 Hz (right). The major phase variations between cardiovascular oscillations and respiration are better evidenced at 0.15 Hz since 88% of the class III patients are above an arbitrary dotted line, whereas only 50% are above the line at 0.25 Hz. All the controls are under the limit whatever the respiratory rhythm.

 

   4. Discussion
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
Appendix A. Complex demodulation
 Appendix B. Pseudo Wigner-Ville...
 References
 
The main findings of the study were as follows: (i) continuous phase variations between cardiovascular oscillations and the respiratory signal were evidenced in CHF patients, the slower the breathing rate, the greater the phase variation (RR/Resp; 0.25 Hz, 23±17°; 0.15 Hz, 46±57°; P<0.01, 0.25 vs. 0.15 Hz). Phase was constant in controls. (ii) The amplitude of the cardiovascular oscillations in the HF domain was more markedly altered when the breathing rate was slowed down, as compared to controls. (iii) Instant parameters evaluated by the complex demodulation method provide new insight into the dynamics of these physiological signals.

4.1. Central regulatory mechanisms of cardiovascular and respiratory rhythms
We used voluntary paced breathing to create periodic forced stimulation of cardiovascular rhythms [17]. The interaction between the cortical centre of voluntary breathing and respiratory neurones in the brainstem is still not completely understood [18]. Stimulation of a specific area of the cerebral cortex increases the respiratory rate and modulates the response amplitude in monkeys, cats and dogs [19]. In humans, specific regions of the brain cortex have been shown to be active during volitional breathing [20]. The forebrain can transmit signals to the respiratory system along an independent pathway, bypassing the automatic metabolic control centre (spontaneous breathing) in the brain stem [21]. Furthermore, robust connections exist between the cerebral cortex, the brainstem respiratory motoneurones, and the hypothalamic area [18,19], a cardiovascular and respiratory integrative structure.

Cardiovascular rhythms are modulated by central mechanisms and afferent inputs from arterial baroreceptors, chemoreceptors, cardiac receptors, and stretch receptors in the thorax, including the lungs. During spontaneous breathing in healthy subjects, the baroreflex and cardiopulmonary reflexes contribute to the genesis of both high and low oscillations of heart rate [22]. During voluntary breathing, the role of the central nervous system is mainly involved. Its implication in generating oscillatory components in cardiac rhythms has been emphasised recently [23–26]. Importantly, simultaneous changes of rhythmic organisation in brainstem neurones, respiration, cardiovascular system and EEG have been demonstrated [27].

4.2. Altered central nervous system integration in CHF patients.
The instant amplitude and frequency provide information on the strength and the oscillatory activity of the instant signal, respectively. The instantaneous phase provides information on the coupling between the oscillations in the two signals, illustrating the synchronisation between them. During cardiac failure, the continuous fluctuating phase evidences the specific lack of synchronisation between cardiovascular and respiratory signals: we predominantly hypothesise a central uncoupling between cardiovascular and respiratory neuronal activities under voluntary breathing.

Autonomic modulation of cardiovascular variabilities in CHF is characterised by alterations at the cerebral level [28,29], sympathovagal imbalance [1] and profound neurohormonal derangements [30]. It should be noted that the patients in our study did not have severe CHF, as indicated by the clinical data and the preservation of the LF oscillations of cardiovascular variabilities [31]. Consistent with this fact, respiratory modulation of autonomic outflow was partly preserved. CHF is associated with changes in specific areas of the brain, where direct alterations in neurone activation in specific area are probably ascribable to exaggerated sympathetic outflow [32]. Furthermore, changes in neuronal activity due to metabolic alteration have been demonstrated [32]. The importance in CHF of a primary impairment of a central mechanism regulating autonomic function has been emphasised [28,29]. However, the origin of this impairment is complex. Peripheral afferent stimuli for the sympathetic system have been suggested, i.e. overactivity of ergoreflexes and chemoreflexes, and reduced activity of baroreflexes and the cardiopulmonary reflexes [32]. Furthermore, during spontaneous breathing, an abnormal primary central rhythm due to variations in the central nervous regulatory activity has been implicated [28,29]. The effects in our study of voluntary and regular slow breathing on autonomic regulation of cardiovascular oscillations and its consequences on the phase variation supports the hypothesis of a central autonomic regulatory impairment in CHF patients. We previously demonstrated in another group of CHF patients that voluntary breathing is associated with alterations in the baroreflex gain of the cardiovascular system: slower paced-breathing decreased the BR gain in CHF whereas the opposite was evidenced in controls. The reversal of the normal pattern of breathing rate-driven BR gain change in the CHF patients suggested a dysfunction of the central neural regulation of autonomic outflow [6].

4.3. Instantaneous phase variation and amplitude estimates
Spectral analysis (by FFT or AR modelling) of cardiovascular oscillations is widely used to assess indexes of neural cardiovascular control and to evaluate the reflex and mechanical interactions between respiration and cardiovascular system. The major drawback of spectral analyses is that they average values (for both frequency and amplitude) obtained throughout the recording period. Thus, they fail to provide information on any time-dependent changes during that period. It follows that instantaneous measurements of biological transients assessed by CDM are of considerable interest for evaluating the dynamics of the interactions between respiratory and cardiovascular system. A constant phase value over time means a close co-ordination between respiratory and cardiovascular system activities. Conversely, transient marked instability in phase signal over time indicates a temporary lack of synchronisation between the respiratory and cardiovascular systems. We focused on the phase fluctuation between the real modulating breathing rate and the cardiovascular signals, i.e. the short-term cardiovascular and respiratory signals synchronisation. Under voluntary breathing the deterioration of phase fluctuation at a specific frequency in chronic heart failure patients mainly suggest a dysfunction of the central neural regulation of autonomic outflow.

4.3.1. Study limitation
We did not measure blood gases during the controlled breathing period and chemoreflexes have direct effects on autonomic outflow. However, it is unlikely that small changes in PO2 or PCO2 at the 0.25 and 0.15 Hz frequencies might influence the continuous phase link changes between the cardiovascular and respiratory signals. Furthermore, CHF patients were receiving drugs (digoxin, diuretics, ACE inhibitors), but it is unlikely that these drugs interfered with the obtained results since differences in the studied parameters were evidenced only when changing the breathing rate.


   Appendix A. Complex demodulation
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 Appendix A. Complex demodulation
Appendix B. Pseudo Wigner-Ville...
 References
 
Cardiorespiratory signals are non-stationary in time, therefore only a time varying method such as CDM, provides some information about the characteristics of the amplitude and frequency fluctuations of the main spectral components of these data. If we suppose that the time series data xt include a component that has a frequency {Lambda} which changes slowly around a reference frequency of {Lambda}0, within the range {Lambda}0±{Lambda}w. The difference between the actual frequency {Lambda} and {Lambda}0 is expressed as the slope of {varphi}t vs. time t, i.e. {Lambda}={Lambda}0+d{varphi}t/dt, xt is written as:


Formula

where At and {varphi}t are the slowly changing amplitude and phase of the component of interest, and zt is residual time series including all other components and noises such as continuous component and trends. The aim of the analysis is to extract approximations of At and {varphi}t as functions of time. Using the Hilbert transform, the Gabor's complex analogue of xt is:


Formula

where Zt is the Gabor's complex signal of zt. We then let Yt be the signal obtained by shifting all the frequencies in Xt by –{Lambda}0: Yt=2Xtexp[–2{pi}{Lambda}0t]. When we let Wt be the signal obtained by passing Yt through a low-pass filter with a cut off frequency of {Lambda}w, we would have Wt=Atexp[j{varphi}t]. Therefore, we can recover amplitude and frequency functions as:


Formula



Formula

Finally, the frequency {Lambda} of the component could be obtained from {Lambda}0 and a first order differential of {Phi}t.


   Appendix B. Pseudo Wigner–Ville distribution
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 Appendix A. Complex demodulation
 Appendix B. Pseudo Wigner-Ville...
References
 
A time–frequency energy distribution largely known and used in physiological signal analysis is the Wigner–Ville distribution (WVD). It provides a representation of the analytical signal x(t) in the joint time–frequency domain, however, it cannot be interpreted as an energy distribution in the exact sense of the term as it may assume a negative value as well. The most important features of this distribution are related to its marginal property: the zero order local moment in the frequency variable is equal to the instantaneous power of the signal (time marginal energy); while the zero order local moment in the time variable is equal to the power spectral density of the signal (frequency marginal). Ville showed that the first moment of the WVD with respect to frequency yields to instantaneous frequency of a signal corresponding to a single spectral component. It is important to underline that when the signal is multicomponent, i.e. s(t)=a1exp(j{omega}1t)+a2exp(j{omega}2t), it must be previously filtered, separating each component to assess frequency and power, respectively.

As in the general Cohen's distribution, two components of different frequency interfere in WVD, producing oscillatory cross-terms in the spectra. These artefacts assume negative values as well and are located in the middle of two auto-components. Averaging the spectra in the time and frequency directions is effective for reducing cross terms and negative regions. Therefore, the smoothed pseudo Wigner–Ville distribution (SPWVD) is well-suited for physiological signal analysis:


Formula

where X(n) is the sampled Gabor's complex signal of x(n). Even if marginal property is no longer valid in SPWVD, we used it to obtain an approximation of instantaneous power and frequency of spectral components in cardiorespiratory oscillations.


Formula



Formula

where Xf is the filtered signal in the frequency band of interest.

Because CDM provides the magnitude of individual components as amplitude, the power obtained by SPWVD was converted to instantaneous amplitude as follows: instantaneous amplitude =Formula where {alpha} was a constant assessed by simulations.


   References
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 Appendix A. Complex demodulation
 Appendix B. Pseudo Wigner-Ville...
 References
 

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