Exp Physiol 92.4 pp 677–686 677

Experimental Physiology

Wavelet analysis of autonomic outflow of normal subjectson head-up tilt, cold pressor test, Valsalva manoeuvre anddeep breathing

J. L. Ducla-Soares1, M. Santos-Bento1, S. Laranjo1, A. Andrade2, E. Ducla-Soares2, J. P. Boto3,

L. Silva-Carvalho1 and I. Rocha1

1Institute of Physiology, Faculty of Medicine of Lisbon, Avenue Professor Egas Moniz, 1649-028 Lisbon, Portugal, Unit of Autonomic Nervous System,

Instituto de Medicina Molecular, Lisbon, Portugal2Institute of Biophysics & Biomedical Engineering, Faculty of Sciences of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal3CMAF, Lisbon, Portugal

Non-invasive autonomic evaluation has used fast Fourier transform (FFT) to assign a range of

low (LF) and high frequencies (HF) as markers of sympathetic and parasympathetic influences,

respectively. However, FFT cannot be applied to brief transient phenomena, such as those

observed on performing autonomic tests where the acute changes of cardiovascular signals

(blood pressure and heart rate) that represent the first and most important stage of the autonomic

performance towards a new state of equilibrium occur. Wavelet analysis has been proposed as a

method to overcome and complement information taken exclusively in the frequency domain.

With discrete wavelet transform (DWT), a time–frequency analysis can be done, allowing the

visualization in time of the contribution of LF and HF to the observed changes of a particular

signal. In this study, we evaluate with wavelets the acute changes in R–R intervals and systolic

blood pressure that are observed in normal subjects during four classical autonomic tests: head-

up tilt (HUT), cold pressor test (CPT), deep breathing (DB) and Valsalva manoeuvre (VM).

Continuous monitoring of ECG and blood presure was performed. Also LF, HF and LF/HF were

calculated. Consistent with previous interpretations, data showed an increase of sympathetic

activity in HUT, CPT and VM. On DB, results reflected an increase in parasympathetic activity

and frequencies. In conclusion, when compared with FFT, wavelet analysis allows the evaluation

of autonomic variability during short and non-stationary periods of time and may constitute a

useful advance in the assessment of autonomic function in both physiological and pathological

conditions.

(Received 28 March 2007; accepted after revision 26 April 2007; first published online 27 April 2007)

Corresponding author I. Rocha: Instituto de Fisiologia, Faculdade de Medicina de Lisboa, Avenue Professor Egas Moniz,

1649-028 Lisbon, Portugal. Email: isabelrocha@fm.ul.pt

Together with the endocrine and the immune systems,the autonomic nervous system (ANS) contributes to themaintenance of homeostasis by controlling the functionof almost every organ. Its dysfunction may result fromprimary neurological diseases such as pure autonomicfailure and multiple system atrophy (Ducla-Soares et al.1993; Consensus Committee of the American AutonomicSociety and the American Academy of Neurology, 1996;Mathias & Polinsky, 1999; Goldstein et al. 2002; Kaufmann& Biaggioni, 2003) or secondary to a wide range of diseaseseliciting a severe morbidity and increased mortality, ashas been clearly shown in diabetes (Sullivan & Feldman,

2005; Maser & Lenhard, 2005; Freeman, 2005) and chronicliver disease (Coelho, 1996; Low, 1998; Sousa & Saraiva,2003). Together with a detailed history and physicalexamination, the laboratory evaluation of ANS functionhas become an essential step for the evaluation of manyclinical conditions and the establishment of an effectivetherapeutic programme. Provocative manoeuvres suchas head-up tilt, cold pressure test, deep breathing andValsalva manoeuvre evoke changes in blood pressureand heart rate that can be used to evaluate autonomicparasympathetic and/or sympathetic outflows indirectly.With these approaches, autonomic behaviour is inferred

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678 J. L. Ducla-Soares and others Exp Physiol 92.4 pp 677–686

from the measurement of cardiovascular variables butno direct quantification of autonomic balance and therelative contribution of its two branches, sympathetic andparasympathetic systems, are made.

The analysis of heart rate and blood pressurevariability by applying fast Fourier transform (FFT) andautoregressive spectral analysis to these signals has madea very important contribution to autonomic evaluation.In particular, FFT decomposes the signals into a seriesof sine and cosine functions of different frequencies andamplitudes, allowing the definition of a power spectrumin which three major ranges of frequencies for humansubjects can be recognized: very low frequencies (VLF,0–0.04 Hz), low frequencies (LF, 0.04–0.15 Hz) and highfrequencies (HF, 0.15–0.4 Hz; Task Force of the EuropeanSociety of Cardiology and the North American Societyof Pacing and Electrophysiology, 1996). The LF bandhas been related mainly to sympathetic outflow, whileHF is related to parasympathetic outflow and respiratoryrhythm. The LF/HF ratio is widely used as an indicatorof the balance between sympathetic and parasympatheticoutflows (Malliani et al. 1991a, 1994; Zaza & Lombardi,2001). However, the application of FFT has an importantlimitation (Duhamel & Vetterli, 1990; Parati et al. 1999),which is that it requires a stationary signal and a longperiod of data collection (at least 5 min), making it anunsuitable tool for the analysis of short and transientchanges of blood pressure and heart rate that occur duringautonomic tests such as those described above. Waveletanalysis has been proposed (Mallat, 1998; Wiklund et al.2002; Mainardi et al. 2002; Postolache et al. 2004; vanden Berg et al. 2005, Urbancic-Rovan et al. 2006; Belovaet al. 2007) as a method to overcome and complementinformation taken exclusively in the frequency domain.With discrete wavelet transform (DWT), a time–frequencyanalysis can be done to allow the visualization in time ofthe contribution of LF and HF to the observed changes ofa particular signal.

In the present study, we describe the application ofwavelet analysis to the acute changes of blood pressure andheart rate signals recorded during standard manoeuvresused to evaluate autonomic function (head-up tilt, HUT;cold pressor test, CPT; deep breathing, DB; and Valsalvamanoeuvre, VM) and we discuss its usefulness for theinvestigation of the autonomic function.

A preliminary report of part of this study wascommunicated to the European Federation of AutonomicSocieties (Silva-Carvalho et al. 2006; Ducla-Soares et al.2006).

Methods

Seventeen healthy volunteers (14 females and 3 males)with a mean age of 44 ± 13 years were included inthis study. None of these subjects had clinical signs of

cardiovascular, neurological or metabolic disorders andnone was under medication. Tests were performed in adedicated autonomic laboratory, in a quiet environmentwith controlled temperature and humidity, during themorning, after a light breakfast without ingestion ofcaffeine or other xanthines. Alcohol and tobacco were notallowed in the previous day and on the day of the test. Somesubjects were unable to perform all the tests adequately,so these results have been withdrawn. Hence, the coldpressor test was validated in only 15 of the 17 subjects(12 females and 3 males, mean age 42 ± 14 years) andthe Valsalva manoeuvre and deep breathing tests in14 of the 17 subjects (3 females and 11 males, mean age43 ± 13 years). Arterial blood pressure and ECG weremonitored continuously using a Task Force Monitor(CNSystems, Reininghaustrasse, Graz, Austria). Studieswere approved by the Ethics Committee of the Facultyof Medicine of Lisbon and performed under informedconsent according to the Declaration of Helsinki.

Experimental protocol

After a rest period of 15 min in the supine position, thesubject was head-up tilted to a level of 60 deg on anelectrical table at a constant speed for 7 min and thenreturned to the horizontal position. The duration of thetilting manoeuvre to and from the supine position wasof 15 s for each change of position. Following recoveryto a supine position, a second rest period of 15 min inthe supine position was allowed to guarantee a stablecondition, after which the subject’s right hand wasimmersed in ice-cold water (4◦C) for 1 min. The subjectwas instructed to breath normally and to avoid sustainedinspiration that would mimic a Valsalva manoeuvre. Aftera third resting period of 15 min in a sitting position, thesubject was instructed to breath, for a period of 15 s, againsta pressure of 40 mmHg after a deep inspiration (Valsalvamanoeuvre). After this manoeuvre, a final rest period of15 min was allowed to elapse before the same subject wasinstructed to breath deeply at a rate of 6 breaths min−1,guided by a metronome, for a period of 1 min (deepmetronomic breathing).

Data analysis

Data were analysed using discrete wavelet transform(Db12; see signal acquisition and processing sectionbelow) applied to systolic blood pressure (SBP) and R–R intervals (RRI) derived from arterial blood pressure andECG, respectively. For each autonomic test, data analysiswas done as follows.

Head-up tilt. The analysis of SBP and RRI was done onfour periods: (1) the last 2 min of the resting period, asa control (CTR); (2) during the 15 s that involved thechange from supine to standing position (TT); (3) at

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Exp Physiol 92.4 pp 677–686 Wavelets and autonomic manoeuvres 679

60 deg, during the first minute of tilt adaptation (TA1);and (4) at 60 deg, during the first minute after TA1 (TA2).In each of these 1 min periods, data analysis was performedin periods of 10 s, the period of 10 s that had the largestchange from maximum to minimum was the one chosento compare with CTR.

Cold pressor test. The analysis of the cardiovascularvariables was done during two periods: (1) the last 2 minof the resting period just prior to the test (CTR); and(2) during 1 min, divided into six epochs of 10 s, the periodof 10 s with the larger change from maximum to minimumwas the one chosen to compare with CTR.

Valsalva manoeuvre. The analysis of SBP and RRI wasdone on three periods: (1) the last minute of the restingperiod just prior to the test (CTR); (2) during 15 s of theValsalva manoeuvre (VM); and (3) during the next 35 safter VM (postVM).

Deep breathing test. Data analysis was done on twoperiods: (1) the last minute of the resting period just priorto the test (CTR); (2) during 1 min of DB, divided intosix epochs of 10 s, the period of 10 s that had the largestchange from maximum to minimum was the one chosento compare with CTR.

Signal acquisition and processing

All data were acquired at 1 kHz and were analysed on thetime–frequency domain using discrete wavelet transform(DWT) using wavelet Daubechies 12 (Db12). This wasselected because the shape of this particular waveletresembles the type of feature present in the time series(Postolache et al. 2003, 2004).

Data were processed in an Origin environment(OriginLab, Origin Lab Scientific Graphing and AnalysisSoftware, Origin Lab Corporation, Northampton, MA,USA). A peak-to-peak routine was implemented inorder to detect blood pressure and ECG peaks and toreconstruct the time evolution curve of systolic bloodpressure and heart rate, which was then computed inan Matlab environment (MathWorks, Natick, MA, USA).The RRI and SBP were interpolated by cubic spline andresampled (resampling period = 0.193 s) to ensure thatthe centre of the frequency range of interest matchedthe central frequency associated to one of the scales ofthe DWT analysis. The Matlab Wavelet Toolbox (Matlab,MathWorks) was used to implement the DWT Db12analysis of the resampled data.

Briefly, the resampled RRI and SBP time series weredecomposed (f RR(t) and f SBP(t)) into a sum of details andapproximation at different scales of resolution. The centralfrequency associated with each scale, f c, was calculatedby f c = Fc

a�where Fc is the central frequency of the used

wavelet, a is equal to 2−j where the scale is j and � isthe resampling period. The signal was decomposed in12 scales. The wavelet transform of the analysed signalsat scale j and position μ is computed using the relation

W f (μ, j) =+∝∫

−∝

f (t)1√2 j

�

(t − μ

2 j

)dt

where � is a wavelet function, Wf (μ, j) are the waveletcoefficients at scales and f (t) either f RR(t) or f SBP(t).

The selected wavelet coefficients for each detail relateto signal frequencies between 0.038–0.15 Hz (LF) and0.15–0.6 Hz (HF; Task Force of the European Society ofCardiology and the North American Society of Pacingand Electrophysiology, 1996). The square of the detailsamplitude (SqA) was then calculated and, for a certaininterval of time, the representative value of LF and HFwas considered as the average of SqA across the detailsassociated with the frequency ranges of interest. Waveletanalysis was implemented on data collected at the differenttime periods, as described above.

Statistical analysis

For CPT and DB, statistical analysis of the differences ofLF, HF and LF/HF between the mean of control valuesand the mean values of each individual period of analysiswas made using Student’s unpaired t test and differenceswere considered significant where P < 0.05 (GraphPADInstruments). Statistical analysis for HUT and VM datawas performed using repeated ANOVA (Bonferroni test).All data were expressed as means ± s.e.m.

Results

Head-up tilt

In all subjects, head-up tilt evoked the characteristicfall in systolic blood pressure followed by a recoveryaccompanied by a further increase followed by a decreaseof heart rate (Low, 1997; Imholz et al. 1990), as shown inFig. 1.

Wavelet analysis (Db12) of SBP values showed asignificant increase of the LF band during TA1 periods(Fig. 2, left panels, and Table 1). No significant changeswere observed in HF bands (see Table 1).

The RRI values computed by wavelets (Db12) showed asignificant increase of the LF band during the TA1 periodbut a significant decrease of the HF band in the TA1 andTA2 periods (Fig. 2, right panels, and Table 1). The LF/HFratio for RRI increased significantly during the TA1 andTA2 periods (Table 1).

Cold pressor test

The cold pressor test provoked the typical progressiveincrease of blood pressure (Hilz & Dutsch, 2006) that

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680 J. L. Ducla-Soares and others Exp Physiol 92.4 pp 677–686

Table 1. Changes in autonomic spectral parameters on head-up tilt

CTR TT TA1 TA2

LFRRI (ms2) 1183 ± 262.3 1451 ± 373.0 2355 ± 446.6∗ 1496 ± 393.8HFRRI (ms2) 921 ± 291.3 749 ± 161.0 394 ± 87.6∗ 383 ± 129.0∗LF/HFRRI 2.1 ± 0.36 3.0 ± 0.70 7.7 ± 1.09∗ 8.4 ± 2.73∗LFSBP (mmHg2) 4.1 ± 0.73 10.1 ± 3.18 15.1 ± 3.75∗ 6.2 ± 1.35

The significant changes in autonomic sympathetic and parasympathetic outflows from the RRI and SBP signal processingevoked by HUT are shown here for each of the four periods of analysis (for abbreviations see text). Note the increasein sympathetic outflow revealed by both RRI and SBP signals. Data are expressed as means ± S.E.M. (∗P < 0.05 betweencontrol and each individual period, n = 17).

Figure 1. Raw data showing systolic blood pressure and heartrate changes during a head-up tilt test in a normal subjectTop panel shows 3 of the 4 periods of analysis: control period just priorto the test (CTR); tilting period (TT); and the first minute of tiltadaptation (TA1). The TA2 period, corresponding to the secondminute of adaptation, is not shown.

persisted during the time of immersion (Fig. 3, left panels).Wavelet analysis (Db12) of the SBP signal showed anincrease in LF and the LF/HFSBP ratio without significantchanges in HF values (Fig. 3, right panels).

Figure 2. Changes on systolic blood pressure and heart rate during head-up tilt analyzed by wavelettransformLeft panels show the relation between the changes in systolic blood pressure during a head-up tilt (HUT) and thesympathetic outflow underlying it. The changes in sympathetic outflow are significant during the change fromsupine to upstanding position (TT) and during the 1 min adaptation period in the standing position, as shownin Table 1. On the right, the RRI signal (top panel) shows an increase in heart rate induced by a decrease inparasympathetic outflow, as expressed by a decrease in HF values (bottom panel).

Valsalva manoeuvre

In all subjects, the Valsalva manoeuvre, a sustainedexpiratory effort or strain against a closed glottis, evokedthe characteristic changes in blood pressure and heartrate (Felker et al. 2006). Four clearly defined phases wereevident: phase I, an increase in blood pressure due to theonset of the strain and a decrease in heart rate; phase II,a sharp decrease in blood pressure below baseline levelsduring the maintenance of the strain that was followed byan increase in pressure accompanied by an increase in heartrate; phase III, a short decrease in arterial pressure relatedto the release of the strain and an increase in heart rate; andphase IV, an overshoot of blood pressure and bradycardia(Fig. 4).

Wavelet analysis (Db12) of SBP values showed asignificant increase of the LF band during both VM andpostVM periods (Fig. 5, left panels, and Table 2).

The RRI values computed by wavelets (Db12) showeda significant increase of the HF band during the postVMperiod (Fig. 5, right panels, and Table 2).

Deep breathing

The deep metronomic breathing test is one of themost used autonomic tests that attempts to standardize

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Exp Physiol 92.4 pp 677–686 Wavelets and autonomic manoeuvres 681

respiratory changes and their relation to heart rate, henceto vagal activity. An expiratory–inspiratory difference orratio can be calculated from the maximum and minimumheart rate values during testing (Genovely & Pfeifer, 1988;Mathias & Bannister, 1999; Hilz & Dutsch, 2006). Thedeep breathing test elicited the typical modulation of bloodpressure by respiration and particularly marked changesin R–R intervals (Fig. 6).

On wavelets analysis (Db12) of the RRI signal, theparasympathetic band is shifted to the frequency rangeof the LF band with a significant increase of its amplitude(Fig. 6, middle and bottom panels).

Discussion

The primary result of this study is to providequantification, using wavelets, of the autonomic outflow,both sympathetic and parasympathetic, involved in theacute adaptation of blood pressure and heart rate duringfour classical autonomic testing manoeuvres: head-uptilt, cold pressor test, Valsalva manoeuvre and the deepbreathing test.

The variability of biological signals, mainly heart ratesignals, was initially regarded as a disturbance of thephysiological rhythm until 1965, when Hon and Leereported a possible clinical significance for absence orreduction of variability in the fetal heart rhythm (Hon& Lee, 1965). Also, with the work of Ewing et al.(1985, 1991), a relation between a reduced heart ratevariability and the degree of autonomic neuropathy indiabetic patients was inferred. Since then, the use of non-invasive methodologies to evaluate autonomic functionhas increased, and both time domain and frequencydomain methods are used to analyse biological signalvariability. The fast Fourier transform in the frequency

Figure 3. Wavelet analysis of systolic blood pressure changes during cold pressor testThe top left panel shows a raw signal of systolic blood pressure, illustrating the progressive increase in pressureduring the immersion of the hand of a normal subject in ice-cold water. The bottom left panel shows the variationof LF related to the changes in systolic blood pressure. The average increase of LF is shown in the bar graphs onthe right and is reflected on the rise of LF/HF ratio (∗P < 0.05, n = 15).

Table 2. Modifications of LF and HF band values during theValsalva manoeuvre

CTR VM PostVM

LFRRI (ms2) 1298 ± 365.7 4596 ± 1191.3∗ 5270 ± 1363.8∗HFRRI (ms2) 587 ± 143.1 915 ± 254.8 1619 ± 445.2∗LFSBP (mmHg2) 5 ± 0.9 41 ± 7.9∗ 22 ± 6.0∗

The significant changes in LF bands are shown between controlvalues versus VM and control values versus postVM periods; alsoshown are the significant changes in the HF frequency bandbetween control and postVM values (for abbreviations see text).Data are expressed as means ± S.E.M. (∗P < 0.05 between controland each individual period, n = 14).

domain has been used most commonly. The computedresult of FFT is a power spectrum which indicates thefrequency and the power of the different componentsof the signal. The FFT spectrum shows a set of threebands, two of which have autonomic significance: theHF reflecting parasympathetic activity and the LF, despitebeing more related to sympathetic activity, giving anindication of the relation between sympathetic andparasympathetic outflow (Task Force of the EuropeanSociety of Cardiology and the North American Societyof Pacing and Electrophysiology, 1996). However, FFT canonly be applied when the signal is stationary and with aduration of at least 5 min. To overcome this deficit, in thepresent study we have used a different method of time–frequency domain analysis. The discrete wavelet transformhas been shown by us (Postolache et al. 2003, 2004; Silva-Carvalho et al. 2006; Ducla-Soares et al. 2006) and others(Mallat, 1998; Wiklund et al. 2002; Mainardi et al. 2002;van den Berg et al. 2005, Urbancic-Rovan et al. 2006;Moriguchi et al. 2006) to be applicable to cardiovascularsignals.

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682 J. L. Ducla-Soares and others Exp Physiol 92.4 pp 677–686

Figure 4. Systolic blood pressure (SBP) and heart rate (HR)changes for a normal subject during the Valsalva manoeuvre(VM)The four characteristic phases of VM are shown: phase I, an increase inblood pressure owing to the onset of the strain and a decrease inheart rate; phase II, a sharp decrease in blood pressure below baselinelevels during the maintenance of the strain followed by an increase inpressure accompanied by an increase in heart rate; phase III, a shortdecrease in arterial pressure related to the release of the strain and anincrease in heart rate; and phase IV, an overshoot of blood pressureand bradycardia.

Wavelets are oscillating mathematical functions ofshort duration that separate a signal into its differentcomponent frequencies and then analyse each frequencywith a resolution matched to its scale. The signal isdecomposed into approximation and detailed signals at

Figure 5. Details of wavelet analysis (Db12) of SBP and RRI signals recorded from a normal subject,showing the underlined changes of sympathetic (seen on LF squares panels) and parasympatheticoutflows (seen on HF squares panels)Note that the increase in amplitude refers to an increase of variation but does not indicate its direction.

several scales (Postolache et al. 2003, 2004; Marques-Neveset al. 2004). There are several types of wavelet functionsbut, in our opinion, those which appear most suitablefor blood pressure and heart rate signal profiles belong tothe Daubechie family (Postolache et al. 2004). The mainadvantage of using a wavelet-based approach to performa time–frequency analysis of physiological signals is theability to capture more efficiently transient fluctuationsin a non-stationary signal in specific frequency bands.Wavelet methodologies have been fine-tuned over theyears for time-dependent spectral analysis (Mallat, 1998;Resnikoff & Wells, 1998). Presently, it is possible to choosefrom a wide range of convolution kernels, accordingto the specific features of the signal to be analysed.Another important point is that wavelet analysis providesa resolution in time and frequency that is dependent on thefrequency. In other words, the detection of resolution intime and frequency is dependent on the central frequencyunder analysis. In many cases, capturing the relativefrequency changes of a physiological signal, as opposedto absolute changes, is most important. For instance, a0.5 Hz change would be more meaningful if the centralfrequency was 2 Hz rather than 30 Hz. Therefore, in manycases it is more desirable to have a better frequencyresolution at lower frequencies, and that is a reason tochoose wavelet analysis. It should also be noted that thewavelet decomposition is, nowadays, a widely acceptedapproach for performing time–frequency analysis of heartrate signals (see Petretta et al. 1999; Verlinde et al. 2001;Toledo et al. 2003).

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Exp Physiol 92.4 pp 677–686 Wavelets and autonomic manoeuvres 683

In relation to the physiological significance of LF andHF bands, from pharmacological studies (Akselrod et al.1981, 1985) it became clear that the HF band is under thecontrol of the parasympathetic system and is related to therespiratory rhythm. The full significance and origin of LFvalues are not yet completely understood but, accordingto Malliani (2000), the LF characterizes sympatheticexcitation independently of its genesis. However, anotherindex, the LF/HF ratio, is also important, since it allowsthe quantification of the relation between the two branchesof the autonomic nervous system in certain physiologicalor pathophysiological conditions (Malliani et al. 1991b).In the present study, by choosing the limits of each detail,we studied the contribution of two sets of frequencies, LF(0.038–0.15 Hz) and HF (0.15–0.6 Hz; Task Force of theEuropean Society of Cardiology and the North AmericanSociety of Pacing and Electrophysiology, 1996), to rapidand acute changes in heart rate (using RRI) and systolicblood pressure evoked by each of the four autonomic testsdescribed.

In head-up tilt (HUT), where an orthostatic challengeis performed, a decrease of blood pressure related toa redistribution of blood by gravity is observed which,consequently, affects the baroreceptor reflexogenic areas toevoke a baroreceptor unloading (Rowell, 1984). Classically,in HUT two phases of adaptation have been described:an earlier cardiovascular acute response that could beobserved during the first 30 s and a second phase,the stabilization period, that itself is composed of twoperiods, an early adaptation period occurring 1–2 min

Figure 6. Deep breathing RR intervals analyzed by wavelet transformTop panel shows RRI variations of a normal subject during deep metronomic breathing. Middle panel shows waveletdetails of RRI signal analysis, illustrating the increase in parasympathetic activity and the shifting of these variationsto the LF range. Bottom panel is a bar graph illustrating the significant changes in HF values between control anddeep breathing values (∗P < 0.05, n = 14).

after orthostasis and a second period related to prolongedorthostasis that lasts for more than 5 min (Hilz & Dutsch,2006); FFT is usually applied to this last period. As anoverall consequence of this manoeuvre, an activation ofthe sympathetic nervous system occurs, together witha decrease of parasympathetic activity acting to re-establish blood pressure levels. Our results show clearlythe modifications of autonomic outflow, as seen in LF,HF and LF/HF values of RRI and LF of SBP, thatunderlie the changes of blood pressure and heart rateduring all the four periods of observation (in a totalperiod of analysis of 195 s) but which are particularlyimportant in the TA1 period. During TA1, the changein the sympathetic/parasympathetic activity shown by theLF/HF index derived from RRI analysis reflects an increaseof sympathetic outflow that is responsible for the increasein heart rate (and, in this case, indirectly for a decrease ofRRI).

The cold pressor test is used to activate pain andtemperature fibres by the immersion of one hand and armin ice-cold water (0–4◦C) for 40–180 s (Hilz & Dutsch,2006). The sympathetic nervous system is activatedthrough the spinothalamic tract, inducing an increase inblood pressure and heart rate, but systolic blood pressure isthe more reliable measurement (Fasano et al. 1996; Ceveseet al. 2001). During this test, we measured the acute andrapid changes in systolic blood pressure that have beencompared between the resting period and the maximumresponse during the immersion of the subject’s hand in icedwater. The direct relation between sympathetic outflow

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684 J. L. Ducla-Soares and others Exp Physiol 92.4 pp 677–686

and the increase of this cardiovascular variable is also welldescribed in the present work through the computation ofthe SBP signal by wavelets. Wavelet analysis demonstratesan increase of LF induced by cold, with no significantchanges in HF. This confirms, using a different techniqueof analysis, the results observed by other authors showingthat in terms of autonomic influences on cardiovascularvariables such as blood pressure and heart rate, thesympathetic nervous system has a more direct influenceon blood pressure, whereas the parasympathetic nervoussystem predominantly affects heart rate. Also, the focus onblood pressure analysis during CPT is in accordance withthe Ewing protocol that classically only evaluates bloodpressure when the cold stimulus is applied, as justified bythe reasons given above.

In the Valsalva manoeuvre, intrathoracic pressureincreases abruptly owing to forced expiration againsta closed glottis, and this causes an increase in heartrate mediated by the carotid baroreceptor reflex (Levin,1966; Ekholm & Erkkola, 1996). In fact, inputs from thearterial baroreceptors play the main role in the reflexcompensatory response to the increase in intrathoracicpressure (Sharpey-Schafer, 1955; Eckberg, 1980; Daly,1997; Looga, 2005). The cardiovascular responses elicitedduring the Valsalva manoeuvre can be divided into fourmain phases (see Results). During phase I of VM, a smallincrease in pressure and a decrease in HR were observedand in phase II, the decrease of blood pressure unloadsthe baroreceptors, thus evoking an increase of sympatheticoutflow. This is shown in our analysis as an increase in LFin SAP and RRI power. After the release of the respiratorystrain in phase III, there is a decrease in blood pressure andan increase in heart rate. Phase IV of VM is characterizedby an increase in blood pressure that evokes a baroreflex-mediated bradycardia, which is also well shown in the HFchanges taken from the RRI signal analysis.

The most common means of eliciting parasympatheticactivation is by deep breathing at 6 breaths min−1. At thisrespiratory rate, the processing of the RRI signal shows ashift of parasympathetic-mediated changes in the HF tothe LF range, with a significant increase in the amplitudeof the observed band (Malik, 1998). Accordingly, theparasympathetic outflow should be analysed on the LFrange of frequencies, since this shifting is due to the factthat the respiratory rate is coincidental with the LF rangeof frequencies. This analysis is only valid in conditions inwhich the frequency and breathing volume are carefullycontrolled (Hartikainen et al. 1998).

With the present results, we also intended to relate theclinical evaluation of autonomic function by the standardautonomic tests performed routinely in an autonomiclaboratory to the autonomic responses elicited by eachmanoeuvre analysed in a more detailed way by wavelets.In fact, in clinical practice it is accepted that heart ratechanges during the Valsalva manoeuvre or deep breathingtest reflect parasympathetic function, whilst the increase

in arterial blood pressure during the cold pressor testreflects sympathetic outflow (Ewing & Clarke, 1982; Ewinget al. 1985; Low, 1997). Also, part of the routine testing ofautonomic function is the evaluation of blood pressureresponses to head-up tilt (Ewing & Clarke, 1982; Ewinget al. 1985; Low, 1997). That is the reason why in thisstudy we focused mainly on blood pressure and heart rateresponses to head-up tilt, blood pressure responses to thecold pressor test and heart rate responses to the Valsalvamanoeuvre and deep breathing test. The present resultsclearly demonstrate the relative change in autonomicactivity that is responsible for the cardiovascular changesin all four tests.

In conclusion, wavelet analysis provides newopportunities for analysing acute and transient changes incardiovascular (and autonomic function) variables withinperiods of less than 5 min which are not accessible to FFT.Also, by overcoming the FFT requirement of a long andstationary signal recording, the analysis of variability ofbiological signals in the time–frequency domain usingwavelets is a useful tool to evaluate autonomic function inroutine medical practice.

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Acknowledgements

The authors are especially grateful to Professor K. Michael Spyer

for his support and help in reviewing this paper.

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