Heart Rate Variability Dynamics During Low-Dose Propofol and Dexmedetomidine Anesthesia MIKA P. TARVAINEN, 1,2 STEFANOS GEORGIADIS, 1 TIMO LAITIO, 3 JUKKA A. LIPPONEN, 1 PASI A. KARJALAINEN, 1 KIMMO KASKINORO, 3 and HARRY SCHEININ 4 1 Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland; 2 Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland; 3 Department of Anaesthesiology and Intensive Care, Turku University Hospital, Turku, Finland; and 4 Department of Pharmacology and Turku PET Centre, University of Turku, Turku, Finland (Received 10 June 2011; accepted 2 March 2012; published online 15 March 2012) Associate Editor Kenneth R. Lutchen oversaw the review of this article. Abstract—Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia. Keywords—Heart rate variability (HRV), Time-varying spec- trum estimation, Kalman smoother, Anesthesia, Propofol, Dexmedetomidine, Loss of consciousness. INTRODUCTION The beat-to-beat variability of the heartbeat interval or heart rate variability (HRV) is a result of autonomic nervous system (ANS) and humoral effects on the sinus node. The ANS can be divided into sympathetic and parasympathetic (also called vagal) branches. Roughly speaking, sympathetic activity tends to increase heart rate and decrease HRV, whereas parasympathetic activity tends to decrease heart rate and increase HRV. 6 The most conspicuous periodic component of HRV is the respiratory sinus arrhythmia (RSA) which is con- sidered to range from 0.15 to 0.4 Hz. This high fre- quency (HF) component is mediated almost solely by the parasympathetic nervous activity. 6,23 Another apparent component of HRV is the low frequency (LF) compo- nent ranging from 0.04 to 0.15 Hz. The LF component is mediated by both sympathetic and parasympathetic nervous activities 6 and is substantially affected by bar- oreflex activity. 9 There are however studies demon- strating that the normalized value of the LF component could be used to assess sympathetic efferent activity. 15,31 Therefore, it is important to examine the LF component power both in absolute and normalized units. Due to the complexity of physiologic control sys- tems, the characteristics of HRV (e.g., powers of LF and HF components) can be assumed to vary in time. HRV dynamics have been addressed in many studies and several time-varying methods have been proposed for estimating changes in HRV spectra; for a good review on these methods and their properties see Chan et al. 10 and Mainardi. 25 These methods include short time Fourier transform (STFT) and wavelet trans- form, 22,33,43 time-frequency distributions such as the Wigner distribution, 26,29,34,42 and time-varying auto- regressive (AR) modeling based methods. 7,8,37 In addition, one fairly recent approach to model the dynamics of HRV is the point process model, 4 which has been applied e.g., for dynamic estimation of RSA component and baroreflex sensitivity. 11,12 Address correspondence to Mika P. Tarvainen, Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland. Electronic mail: mika.tarvainen@uef.fi Annals of Biomedical Engineering, Vol. 40, No. 8, August 2012 (Ó 2012) pp. 1802–1813 DOI: 10.1007/s10439-012-0544-1 0090-6964/12/0800-1802/0 Ó 2012 Biomedical Engineering Society 1802
Transcript
Heart Rate Variability Dynamics During Low-Dose Propofol
and Dexmedetomidine Anesthesia
MIKA P. TARVAINEN,1,2 STEFANOS GEORGIADIS,1 TIMO LAITIO,3 JUKKA A. LIPPONEN,1 PASI A. KARJALAINEN,1
KIMMO KASKINORO,3 and HARRY SCHEININ4
1Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland; 2Department of ClinicalPhysiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland; 3Department of Anaesthesiology andIntensive Care, Turku University Hospital, Turku, Finland; and 4Department of Pharmacology and Turku PET Centre,
University of Turku, Turku, Finland
(Received 10 June 2011; accepted 2 March 2012; published online 15 March 2012)
Associate Editor Kenneth R. Lutchen oversaw the review of this article.
Abstract—Heart rate variability (HRV) has been observed todecrease during anesthesia, but changes in HRV during lossand recovery of consciousness have not been studied indetail. In this study, HRV dynamics during low-dosepropofol (N = 10) and dexmedetomidine (N = 9) anesthesiawere estimated by using time-varying methods. Standardtime-domain and frequency-domain measures of HRV wereincluded in the analysis. Frequency-domain parameters likelow frequency (LF) and high frequency (HF) componentpowers were extracted from time-varying spectrum estimatesobtained with a Kalman smoother algorithm. The Kalmansmoother is a parametric spectrum estimation approachbased on time-varying autoregressive (AR) modeling. Priorto loss of consciousness, an increase in HF component powerindicating increase in vagal control of heart rate (HR) wasobserved for both anesthetics. The relative increase of vagalcontrol over sympathetic control of HR was overall largerfor dexmedetomidine which is in line with the knownsympatholytic effect of this anesthetic. Even though theinter-individual variability in the HRV parameters wassubstantial, the results suggest the usefulness of HRVanalysis in monitoring dexmedetomidine anesthesia.
The beat-to-beat variability of the heartbeat intervalor heart rate variability (HRV) is a result of autonomicnervous system (ANS) and humoral effects on the sinusnode. The ANS can be divided into sympathetic and
parasympathetic (also called vagal) branches. Roughlyspeaking, sympathetic activity tends to increase heartrate and decrease HRV, whereas parasympatheticactivity tends to decrease heart rate and increase HRV.6
The most conspicuous periodic component of HRV isthe respiratory sinus arrhythmia (RSA) which is con-sidered to range from 0.15 to 0.4 Hz. This high fre-quency (HF) component ismediated almost solely by theparasympathetic nervous activity.6,23 Another apparentcomponent of HRV is the low frequency (LF) compo-nent ranging from 0.04 to 0.15 Hz. The LF component ismediated by both sympathetic and parasympatheticnervous activities6 and is substantially affected by bar-oreflex activity.9 There are however studies demon-strating that the normalized value of the LF componentcould be used to assess sympathetic efferent activity.15,31
Therefore, it is important to examine the LF componentpower both in absolute and normalized units.
Due to the complexity of physiologic control sys-tems, the characteristics of HRV (e.g., powers of LFand HF components) can be assumed to vary in time.HRV dynamics have been addressed in many studiesand several time-varying methods have been proposedfor estimating changes in HRV spectra; for a goodreview on these methods and their properties see Chanet al.10 and Mainardi.25 These methods include shorttime Fourier transform (STFT) and wavelet trans-form,22,33,43 time-frequency distributions such as theWigner distribution,26,29,34,42 and time-varying auto-regressive (AR) modeling based methods.7,8,37 Inaddition, one fairly recent approach to model thedynamics of HRV is the point process model,4 whichhas been applied e.g., for dynamic estimation of RSAcomponent and baroreflex sensitivity.11,12
Address correspondence to Mika P. Tarvainen, Department of
Applied Physics, University of Eastern Finland, P.O. Box 1627,
Annals of Biomedical Engineering, Vol. 40, No. 8, August 2012 (� 2012) pp. 1802–1813
DOI: 10.1007/s10439-012-0544-1
0090-6964/12/0800-1802/0 � 2012 Biomedical Engineering Society
1802
HRV has been studied with relation to cardiovas-cular diseases, diabetic neuropathy, physical exercise,mental stress, gender and age, and sleep.1,23,40 Inaddition, anesthetic drugs are known to alter HRVsignificantly. The relationship between ANS activity,as observed through HRV analysis, and general anes-thesia has been quite widely explored and the partiallydivergent effects of different anesthetics have beenidentified.23,28 The effect of general anesthesia on theANS activity is typically evaluated by using stationarymethods, i.e., by applying standard fast Fouriertransform (FFT) on short data segments.14,16,18,24,27,30
In addition, the dynamics of HRV during anesthesiahave been evaluated for example by using smoothedpseudo Wigner–Ville distribution19,41 and wavelettransform.20,33 However, the dynamic changes of HRVaround loss and recovery of consciousness have notbeen studied in detail.
The aim of this study was to examine HRV changesclose to loss and recovery of consciousness. Twoanesthetic drugs with distinct mechanisms of actionwere studied; propofol (GABAergic) which is a short-acting, commonly used hypnotic agent, and dexmede-tomidine (selective alpha2-agonist) which is a sedativemedication increasingly used in intensive care units.Dexmedetomidine was selected because of its knownsympatholytic effect due to which this drug is assumedto produce observable changes in HRV. Propofol is acommonly used and rather widely studied anesthetic,thereby producing a reference to the observed HRVchanges. In the experimental part of the study, con-centrations of the anesthetic drugs were increased instages until loss of consciousness (LOC) was reached inspontaneously breathing subjects. The tests were freefrom any disturbances resulting from surgery, otherdrugs, or ventilator. Thus, the study setup was optimalfor examining HRV changes during loss and recoveryof consciousness.
In this paper, HRV dynamics in healthy volunteersundergoing low-dose propofol and dexmedetomidineanesthesia were assessed through standard time-do-main and frequency-domain measures of HRV. Thefrequency-domain parameters were estimated dynam-ically by using a time-varying parametric spectrumestimation method originally proposed in Tarvainenet al.37 In the method, the RR interval data are mod-eled with a time-varying AR model, the modelparameters are estimated recursively with a Kalmansmoother algorithm, and time-varying spectrum esti-mates are obtained from the estimated model param-eters. The advantages of this spectrum estimationmethod are high time-frequency resolution and thepossibility to decompose the instantaneous spectrumestimates into separate frequency components.37 Thisis especially advantageous in HRV analysis, because it
enables a separate estimation and investigation of theLF and HF components. In this paper, the method wasfurther improved to take into account respiratory fre-quency changes in the HF component estimation. Thisimprovement ensures that the RSA is always capturedwithin the HF component.
METHODS
A selection of time-domain and frequency-domainmeasures of HRV were considered. The selected anal-ysis parameters and their computation are presentedshortly in the following.
Time-Domain Measures of HRV
The time-domain parameters considered in thisstudy were the mean RR interval (RR), standarddeviation of RR intervals (SDNN), and root meansquare of successive differences (RMSSD). Dynamicsof these parameters were computed in a 60 s movingwindow (moved with 1-s steps) for the whole RRinterval series. The mean RR interval at time t wasobtained by averaging the RR intervals within themoving window, i.e.,
RRðtÞ ¼ 1
L
XL
j¼1tj2wt
RRj ð1Þ
where L is the number of RR intervals within a timewindow wt = [t 2 30, t + 30] s and RRj is the jth RRinterval observed at time tj. The SDNN at time t wassimilarly defined as
is a measure of short-term variability. It should benoted that the number of RR intervals (L) within thetime window wt may change from one time instant toanother because of changes in the mean heart rate.
Frequency-Domain Measures of HRV
The frequency-domain parameters considered inthis study were LF and HF component powers as well
Heart Rate Variability Dynamics 1803
as the total spectral power. These power values as afunction of time were obtained from the time-varyingspectra which were estimated using the Kalmansmoother spectrum estimation method. In the spec-trum estimation, the data is assumed to be equidis-tantly sampled, and thus the RR interval series wereconverted into equidistantly sampled signals by usinginterpolation methods. The Kalman smoother spec-trum estimation method is shortly described in thefollowing (for details see Tarvainen et al.37).
In the method, the equidistantly sampled RR serieswas first modeled with a time-varying AR model oforder p defined as
xt ¼ �Xp
j¼1aðjÞt xt�j þ et ð4Þ
where xt is the modeled signal (i.e., the RR series), at(j)
is the value of jth AR parameter at time t and et isobservation error. By denoting
Ht ¼ ðxt�1; . . . ; xt�pÞ ð5Þ
ht ¼ ð�að1Þt ; . . . ;�aðpÞt ÞT ð6Þ
the time-varying AR model can be written in the form
xt ¼ Htht þ et ð7Þ
which is a linear observation model. Kalman smootheralgorithm is based on the so-called state-space for-malism which means that in addition to observationmodel, the evolution of the state (i.e., AR parameters)is modeled. Here a random walk model
htþ1 ¼ ht þ wt ð8Þ
where wt is the state noise term, was used. Equations(7) and (8) form the state-space signal model for xt andthe evolution of the AR parameters can be estimatedby using the Kalman smoother algorithm.
Kalman Smoother Algorithm
The Kalman smoother algorithm consists of aKalman filter algorithm and a fixed-interval smoother.The Kalman filtering problem is to find the linear meansquare estimator ht for state ht given the observationsx1; x2; . . . ; xt: Kalman filter equations can be summa-rized as2
C~htjt�1¼ C~ht�1
þ Cwt�1 ð9Þ
Kt ¼ C~htjt�1HT
t ðHtC~htjt�1HT
t þ CetÞ�1 ð10Þ
ht ¼ ht�1 þ Ktðxt �Htht�1Þ ð11Þ
C~ht¼ ðI� KtHtÞC~htjt�1
ð12Þ
where ~ht ¼ ht � ht is the state estimation error,~htjt�1 ¼ ht � ht�1 is the state prediction error, Kt is theKalman gain vector, and Cet and Cwt
are the observa-tion and state noise covariances, respectively.
The fixed-interval smoothing problem is to findestimates hSt (S denotes smoothed estimates) for eachstate ht given all the observations x1; x2; . . . ; xN: Fixed-interval smoothing equations can be summarized as2
hSt ¼ ht þ AtðhStþ1 � htÞ ð13Þ
C~hSt¼ C~ht
þ AtðC~hStþ1� C~htþ1jt
ÞATt ð14Þ
where At ¼ C~htC�1~htþ1jt
: The smoother algorithm is ini-
tialized with the Kalman filter estimates, i.e., hSN ¼ hNand C~hSN
¼ C~hN: That is, the smoothed estimates hSt are
obtained by first running the Kalman filter algorithmforward in time and then the smoother algorithmbackwards in time.
Adaptation of the Algorithm
The terms effecting the adaptation of Kalmansmoother algorithm are the state noise covariance(Cwt
), observation noise covariance (Cet ¼ r2et) and the
variance of the modeled signal (r2xt). In order to control
the adaptation of the algorithm, the following solutionwas adopted. First, the observation noise variance wasestimated recursively at every step of the Kalman filterequations as
r2et¼ 0:95r2
et�1þ 0:05�2t ð15Þ
where �t is the one step prediction error�t ¼ xt �Htht�1: Secondly, the state noise covariancematrix was selected to be diagonal Cwt
¼ r2wtI; and r2
wt
was adjusted at every step of the Kalman filter equa-tions as
r2wt¼ UCr2
et=r2
xtð16Þ
where r2xtis the estimated variance of the observed RR
series at time t and UC is an update coefficient throughwhich the adaptation of the algorithm is adjusted.Therefore, the adaptation of the algorithm is adjustedwith a single parameter (UC) and a fixed value of UCcan be used for all data.
Time-Varying Spectrum Estimation
Once the time evolution of the AR parameters isestimated with the Kalman smoother, the time-varyingspectrum estimates can be obtained as
TARVAINEN et al.1804
PtðfÞ ¼r2et=fs
j1þPp
j¼1 aðjÞt e�i2pjf=fs j2
ð17Þ
where fs is the sampling frequency, aðjÞt is the jth AR
parameter estimate at time t, and r2etis the variance of
the estimated observation error process at time t. Notethat r2
etis not the variance estimated iteratively in the
Kalman filter forward run, but it is the posterior errorvariance evaluated based on the smoothed estimates.
The total spectral power as a function of time can becomputed from (17) as
PTot:t ¼
Xfs=2
f¼0PtðfÞDf ð18Þ
where Df is the frequency grid interval.LF and HF band powers could be computed simi-
larly by summing over the frequency points within thepredefined frequency bands. However, here the LF andHF powers are computed from decomposed spectralcomponents.
Spectral Decomposition and Component Powers
The AR spectrum given in (17) can be decomposedinto components by presenting it in the factored form
PtðfÞ ¼r2et=fs
Qpj¼1ðz� aðjÞt Þð1=z� aðjÞ�t Þ
ð19Þ
where z ¼ ei2pf=fs ; aðjÞt are the time-varying roots (poles)of the AR polynomial, and * denotes complex conju-gate. Now, consider a pole at
(j) positioned at frequencyfj. The spectrum of this single component Pt
(j)(f) in thevicinity of fj can be estimated by assuming the effect ofother poles on the spectrum to be constant (for detailssee Tarvainen et al.37). Once the spectral componentsfor each time instant t are estimated, LF and HFpowers can be computed by integrating the compo-nents positioned within the defined frequency bands.
In order to capture the RSA component alwayswithin the HF band, the HF (and also LF) band limitsare adjusted according to the respiratory frequency asfollows. The lower limit of the HF band was fixed to benot higher than 0.15 Hz, but in the case of sloweddown respiratory dynamics, the lower limit was ad-justed to be 0.025 Hz below the estimated respiratoryfrequency (but not less than 0.125 Hz). The lower limitof the HF band was however forced to be at least0.025 Hz higher than the peak frequency of the LFcomponent observed at previous time step. If the dif-ference between respiratory rate and LF peak fre-quency would be less than 0.025 Hz, LF and HFpowers would not be computed because the separationof the components could not be trusted. The upper
limit of the HF band was dynamically adjusted to be0.125 Hz above the estimated respiratory frequency(but at least 0.4 Hz). This dynamic adjustment of theHF band ensures that the RSA component is alwayscaptured within the HF band. For other recent meth-ods for fixing the HF band according to respiratoryfrequency see Bailon et al.3 and Goren et al.17 For theLF band, the lower limit was fixed to 0.04 Hz andthe upper limit was between always 0.125–0.15 Hzaccording to the lower limit of the HF band.
The LF and HF powers are then obtained as
PLFt ¼
X
jjffaðjÞt 2LFbandf g
Xfs=2
f¼0PðjÞt ðfÞDf
" #ð20Þ
PHFt ¼
X
jjffaðjÞt 2HFbandf g
Xfs=2
f¼0PðjÞt ðfÞDf
" #ð21Þ
where Pt(j)(f) are the spectral components whose peak
frequencies (i.e., the angles of the corresponding polesffaðjÞt ) are within the specific band.
In addition to the absolute band powers, the LF andHF powers are commonly presented in normalizedunits (n.u.) as
PLFt ðn.u.Þ ¼
PLFt
PTot:t � PVLF
t
100% ð22Þ
PHFt ðn.u.Þ ¼
PHFt
PTot:t � PVLF
t
100% ð23Þ
where PtVLF is the power of the very low frequency
(VLF) band (0–0.04 Hz). These normalized powervalues reveal the relative strengths of LF and HFcomponents. In addition, the ratio between LF andHF powers is often considered as an index of sym-patho-vagal balance.
MATERIALS AND DATA ACQUISITION
Data from subjects undergoing propofol (Prop:N = 10, healthy males, age 19–28 years) and dex-medetomidine (Dex: N = 9, healthy males, age 19–26 years) anesthesia were analyzed. Originally, 20subjects participated the tests after giving their writteninformed consent, but 1 subject from dexmedetomi-dine group was excluded from analysis because ofsnoring (observed in data as clear recurrent decreasesin respiration related chest movements and synchro-nous strong increases in heart rate). The study protocolwas approved by the Ethical Committee of the
Heart Rate Variability Dynamics 1805
Hospital District of Southwest Finland (Turku, Fin-land) and the National Agency for Medicines.
The anesthetic drug was administered intravenouslyusing target control infusion (TCI) aiming at pseudosteady-state plasma concentrations at 10-min intervalsstarting from 1.0 lg/ml (Prop) or 1.0 ng/ml (Dex) andfollowed by 0.25–0.5 lg/ml (Prop) or 0.25–0.5 ng/ml(Dex) increases until LOC was reached. The infusionwas terminated after LOC. The consciousness of thesubject was tested at each concentration level and afterthe infusion termination by asking the subject to openhis eyes. LOC was defined as no response to the ‘‘openyour eyes’’ request and recovery of consciousness(ROC) as a meaningful response to the same request.During the recording session, subjects were not dis-turbed in any other way (except the ‘‘open your eyes’’request); neither any compensatory maneuvers norpharmacological interventions were performed. Thestudy setup is illustrated in Fig. 1.
Electrocardiogram (ECG) along with a set of elec-troencephalogram (EEG) channels were recordedusing a Galileo (Medtronic, Italy) EEG acquisitionsystem. In addition, respiration signal was measuredusing an accelerometer for recording chest movements.The sampling rate of all the signals was 256 Hz. TheR-wave time instances were extracted from the ECGby using an adaptive QRS detection algorithm. Priorto detecting the R-wave peak times, the R-waves wereinterpolated (Fourier interpolation at 2000 Hz) in or-der to improve the time resolution of the detection.The RR interval time series were then formed, andprior to spectral analysis, the RR series were interpo-lated (4 Hz cubic spline interpolation) to have evenlysampled data. Finally, the VLF trend components(frequencies below 0.04 Hz) were removed from the
RR interval series by using a smoothness priorsmethod.39 The respiratory frequency as a function oftime was obtained as the peak frequency of the respi-ration signal spectrum computed in 60 s moving win-dow using FFT.
RESULTS
The Kalman smoother spectrum estimation wasapplied to the preprocessed (i.e., interpolated anddetrended) RR interval series of all subjects. Standardmodel order selection criteria were utilized in ARmodel order selection and on average they suggestedan order p = 16 which was then used for all data. Theupdate coefficient of the Kalman smoother algorithmto UC = 1025. Time-varying analysis of data from tworepresentative subjects (Prop and Dex) is presented inFig. 2. The vertical lines on the figure show the LOC-testing times; the three bold lines representing (1) thelast LOC-test where a response from subject wasobtained (pre-LOC), (2) the first LOC-test wheresubject did not respond (LOC), and (3) the first LOC-test (after LOC) where subject responded (ROC). Thedecompositions of the spectra into LF and HF com-ponents are explicit and for both subjects HF power isincreased around LOC. The figure also shows a nota-ble lengthening of RR interval (decrease in heart rate)for dexmedetomidine.
The selected time-domain and frequency-domainmeasures of HRV were then computed as a function oftime for every subject and for both drugs. Time-do-main parameters RR; SDNN and RMSSD werecomputed from the detrended (but not interpolated)RR interval series according to (1), (2) and (3),respectively. The respiratory frequency was computedas described in ‘‘Materials and Data Acquisition’’section. The total spectral power and powers of LFand HF components were computed according to (18),(20) and (21), respectively. LF and HF powers werealso computed in normalized units according to (22)and (23). The LF/HF ratio was obtained as the ratiobetween absolute LF and HF powers.
In order to statistically evaluate the group levelchanges in the HRV parameters during anesthesia, thewhole study setup was divided into 11 states. Thesestates (shown in Fig. 1) were:
1. Baseline (2 min before infusion start)2. Infusion start (middle point of first concentration
level)3. Infusion middle (middle point from infusion start
to LOC)4. Pre-LOC I (1 min before pre-LOC)5. Pre-PLOC II (1 min after pre-LOC)
BASELINE
CONC 1.0
CONC 2.0
CONC 1.75
CONC 1.5
10 20 30 400 50 min
LOC-testing
Pre-LOC LOC ROC
1 2 3 4 5 6 7 8 9 1110Analysisepochs
FIGURE 1. Illustrative presentation of the study setup. Con-sciousness was tested twice at each drug level and at 1-minintervals after the infusion was terminated. The arrows on thebottom indicate these LOC-testing times and the color of thearrow indicates the result of the LOC-testing (blue 5 -response, red 5 no response). The 11 epochs used later inthe analysis are marked with gray vertical bars.
TARVAINEN et al.1806
6. LOC I (1 min before LOC)7. LOC II (1 min after LOC)8. Infusion End (1 min after end of infusion)9. ROC I (1 min before ROC)10. ROC II (1 min after ROC)11. ROC III (3 min after ROC).
The parameter values for each state (except forbaseline) were computed by averaging the time-varyingparameter values within a 60 second window. Thebaseline values were averaged within a 4-min windowin order to have more reliable values from a statewhich was assumed to be physiologically static.
As a first step, each selected HRV parameter wasstatistically tested for any significant differencesbetween the states. For this task the nonparametricFriedman’s test was applied separately for eachparameter and for the two drugs. Sufficiently small pvalue for a given parameter would suggest that at leastone state is significantly different from the others (i.esignificantly different group median). The significancelevel was set to a = 0.05 and it was corrected formultiple comparisons (over all parameters and bothdrugs) with false discovery rate (FDR)5 to yield athreshold of p £ 0.0334. The results are presented inTable 1, showing that two parameters for propofol
group (respiratory frequency and LF power) and threeparameters for dexmedetomidine group (LF and HFpowers in normalized units and LF/HF ratio) were notfound to have any significant difference between the 11states.
Pairwise comparisons between the states were thenperformed for each parameter and for both drugs inorder to find out the most significant effects of the
0.7
1
1.3
1.6pre−LOC LOC ROC
PropofolR
RI (
s)
Time−varying spectrum
Fre
q. (
Hz)
0
0.2
0.4
HF component
Fre
q. (
Hz)
0
0.2
0.4
LF component
Fre
q. (
Hz)
0
0.2
0.4
00:00:00 00:15:00 00:30:00 00:45:00 01:00:00
15
30
45
Time (h:min:s)
Pow
er (
dB) HF power
LF power
0.7
1
1.3
1.6pre−LOC LOC ROC
Dexmedetomidine
RR
I (s)
Time−varying spectrum
Fre
q. (
Hz)
0
0.2
0.4
HF component
Fre
q. (
Hz)
0
0.2
0.4
LF component
Fre
q. (
Hz)
0
0.2
0.4
00:00:00 00:15:00 00:30:00 00:45:00 01:00:00
15
30
45
Time (h:min:s)
Pow
er (
dB) HF power
LF power
FIGURE 2. HRV dynamics for representative subjects from propofol and dexmedetomidine groups. The axes from top to bottomillustrate: (1) RR interval series, (2) Kalman smoother spectra with respiratory frequency curves, (3) HF component spectra, (4) LFcomponent spectra, and (5) LF and HF powers in decibels. The vertical lines indicate LOC-testing times.
TABLE 1. Friedman’s test results for comparing state effectson the selected HRV parameters.
Parameter Propofol Dexmedetomidine
RR (ms) p = 0.0095** p < 0.0001**
SDNN (ms) p = 0.0123** p = 0.0005**
RMSSD (ms) p = 0.0024** p < 0.0001**
Respiratory freq. (Hz) p = 0.3025 p = 0.0002**
LF power (ms2) p = 0.0664 p = 0.0192**
LF power (n.u.) p = 0.0189** p = 0.1701
HF power (ms2) p = 0.0005** p = 0.0002**
HF power (n.u.) p = 0.0165** p = 0.1727
LF/HF ratio p = 0.0334** p = 0.1305
Total power (ms2) p = 0.0002** p = 0.0006**
Sufficiently small p value suggests that the parameter value is
significantly different at least at any one state compared to other
states.
*p £ 0.05; **p £ 0.0334 (FDR).
Heart Rate Variability Dynamics 1807
anesthetic drugs on HRV. At this point it was notmeaningful to compare all the 11 states, but only themost essential states regarding anesthesia monitoring.Therefore, we selected states 1 (baseline), 4 (pre-LOCI), 6 (LOC I) and 9 (ROC I) for the pairwise com-parisons. Pairwise comparisons between these fourstates were performed with Wilcoxon paired, two-sidedsigned rank test and the results are presented inTable 2. Results are shown only for the most mean-ingful comparisons. The significance level was again setto a = 0.05 and it was corrected for multiple com-parisons with FDR to yield a threshold of p £ 0.0039.Comparisons were performed only for those parame-ters which were found to have significant differencesbetween the original 11 states (see Table 1).
Figure 3 illustrates the group results for the HRVparameters (LF power was excluded as it did not showany significant changes between the states for either ofthe drugs). The plots show the group medians and the25th and 75th percentiles for the selected states and forboth drugs. Individual changes between the states arealso illustrated in the graphs. For the selected states,statistical comparisons between the two drugs wereperformed for every parameter by using the Wilcoxonrank sum test. The significance level was set toa = 0.05 and it was corrected for multiple comparisonswith FDR to yield a threshold of p £ 0.0015. Theresults of between drug comparisons are also illus-trated in Fig. 3.
The between state comparisons revealed the fol-lowing. For propofol, a significant increase in HFpower and total power, an increase in normalized HFpower (p = 0.0195), and decrease in LF/HF ratio(p = 0.0137) were observed from pre-LOC I to LOC I.These parameters did not show any significant changefrom LOC I to ROC I. However, an increase in SDNN(p = 0.0059), an increase in RMSSD (p = 0.0137), andlengthening of RR interval (p = 0.0488) were observedfrom LOC I to ROC I. For dexmedetomidine, a sig-nificant lengthening of RR interval and increase inrespiratory frequency were observed to take place frombaseline to pre-LOC I. More importantly, HF powerand total power increased from pre-LOC I to LOC I(p = 0.0195 and p = 0.0078, respectively) anddecreased from LOC I to ROC I (p = 0.0117 andp = 0.0078, respectively). In addition, normalizedHF power increased from pre-LOC I to LOC I(p = 0.0391), even though this parameter was notfound to have any statistically significant differencebetween the 11 states (see Table 1).
The between drug comparisons revealed that themean RR interval was significantly longer (lower heartrate) for dexmedetomidine at pre-LOC I, LOC I andROC I when compared to propofol. RMSSD washigher for dexmedetomidine at pre-LOC I (p = 0.0222)and LOC I (p = 0.0275).
The changes in the HF and total spectral poweraround LOC and ROC observed using the Kalman
TABLE 2. Pairwise comparisons between baseline, pre-LOC I, LOC I and ROC I.
Parameter Baseline fi pre-LOC I Baseline fi LOC I Pre-LOC I fi LOC I LOC I fi ROC I
FIGURE 3. Box plots of HRV parameters for selected states (baseline, pre-LOC I, LOC I, and ROC I) for propofol (blue) anddexmedetomidine (red). On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, andthe whiskers extend to the most extreme parameter values excluding outliers which are plotted separately. Individual changesbetween all the states (states 1-11) are illustrated with the lines, i.e., each line shows the parameter value at different states for onesubject. The stars (*) indicate statistical differences between the two drugs (*p £ 0.05, **p £ 0.01, ***p £ 0.0015).
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FIGURE 4. Individual HF power trajectories (subjects S1–S9) around LOC and ROC for dexmedetomidine extracted by using theproposed Kalman smoother approach (KS1), standard Kalman smoother approach (KS2), and STFT. Pre-LOC, LOC and ROC timesare illustrated with red vertical lines, whereas pre-LOC I, LOC I and ROC I states used in statistical comparisons are illustrated withgray vertical bars.
Heart Rate Variability Dynamics 1809
smoother spectral estimation approach constitute themain finding of the study. Therefore, individualchanges in these parameters around LOC and ROCwere further evaluated. In addition, the identificationperformance of the proposed Kalman smoother spec-trum estimation approach (KS1) was compared toperformances of standard Kalman smoother spectrumestimation without spectral decomposition and respi-ratory frequency tracking (KS2) and short-time Fou-rier transform (STFT) computed using 120 s movingwindow. Figure 4 shows the individual trajectories ofHF power (both in absolute and normalized units)around LOC and ROC for the dexmedetomidinegroup. The absolute power values are shown for thethree different spectrum estimation methods. Theproposed Kalman smoother and the standard Kalmansmoother approach estimates differ noticeably only forfour subjects (S3, S5, S7 and S9).
From Fig. 4, it is clear that there are big inter-individual differences in the HF power trends as well asin the duration of the LOC. However, there is a certainconsistency in the HF power changes. The statistics ofthe HF and total power changes around LOC (Propand Dex) and ROC (Dex) observed using the differentspectrum estimation methods are presented in Table 3.Overall, the most consistent statistics are obtained withthe proposed spectrum estimation method. In thepropofol group, the HF power increase from pre-LOCI to LOC I is captured with the proposed method forall 10 subjects. In the dexmedetomidine group, the HFpower increase from pre-LOC I to LOC I is capturedfor 8/9 subjects (all except S5) and the decrease fromLOC I to ROC I is captured for 8/9 subjects (all exceptS2, for whom there is also a decrease, but it starts a bitlater). Therefore, for 7/9 subjects a consistent trend inHF power (increase before LOC and decrease before
ROC) is observed for dexmedetomidine by using theproposed Kalman smoother spectrum estimationmethod. However, by visual inspection this trend inHF power is more or less clear for 6/9 subjects (S1, S2,S3, S6, S7, and S9). For two other subjects (S5 and S8),a clear decrease in HF power is observed at ROCbut they don’t show any clear increase in HF beforeLOC.
DISCUSSION
The proposed Kalman smoother spectrum estima-tion approach was observed to produce high time-frequency resolution, and thereby it provided accurateestimation of HRV frequency-domain measures whenmonitoring fast changes in the level of consciousness.A more detailed comparisons of the time-frequencyresolutions of Kalman smoother approach and STFT(also known as the spectrogram) have been made inTarvainen et al.37,38 In Tarvainen et al.,37 it was alsoproposed that the LF and HF powers can be evaluatedmore accurately from the decomposed spectral com-ponents than by using predefined frequency bands.The results of this study, verify this proposition byshowing that the decomposition can have a significanteffect on certain HRV measures even in the group le-vel. In this paper, the Kalman smoother approach wasfurther improved to take into account changes inrespiratory frequency when defining the frequencyband for the HF component. This improvement en-sures that the RSA component is fully included in theHF band and the parasympathetic activity will not beunderestimated.
The aim of the study was to examine HRV changesclose to loss and recovery of consciousness in two
TABLE 3. Comparison of spectrum estimation methods, reproducibility of the main findings.
spectral decomposition and respiratory frequency tracking); STFT = Short-time Fourier transform (120 s window).
TARVAINEN et al.1810
anesthetic drugs, and thereby to evaluate the usefulnessof HRV analysis in anesthesia monitoring. The mostpromising HRV parameters for monitoring purposeswere thus identified. In addition, it was important toidentify the different cardiovascular effects of the twoanesthetics in order to use HRV for supporting anes-thesia monitoring in clinical practice.
The results showed an increase in HF power (indi-cating increase in vagal control of HR) prior to LOCfor both drugs (propofol and dexmedetomidine). Fordexmedetomidine, a strong decrease in heart rate wasalso observed prior to LOC. The relative increase ofvagal control over sympathetic control of HR wasoverall larger for dexmedetomidine showing on aver-age strong decrease in heart rate (62/45 bpm in base-line/LOC), strong increases in RMSSD (53/238 msin baseline/LOC), absolute HF power (700/3180 ms2 inbaseline/LOC), and normalized HF power (47/84% inbaseline/LOC). However, only the decrease in heartrate was found to be statistically different between thetwo drugs. These findings are in line with the well-described cardiovascular effects of dexmedetomidine.The main causes of these effects are its centrallymediated sympatholytic effect (i.e., attenuation ofsympathetic tone) in combination with peripherallymediated vasoconstriction.13 The augmented para-sympathetic outflow has been attributed to the directactivation of alfa2-adrenoceptors in the dorsal motornucleus of the vagus nerve and nucleus tractussolitarius.36
The most promising HRV parameter for anesthesiamonitoring purposes was the absolute HF powerwhich increased from pre-LOC to LOC (202/669 ms2
increase for Prop/Dex). Also the total power increasedfrom pre-LOC to LOC, but this was due to the increasein HF component. For dexmedetomidine, the HFpower and total power decreased again from LOC toROC (2357 and 21318 ms2, respectively), and thusthey could be used also for predicting ROC. For pro-pofol, on the other hand, the most significant changebetween LOC and ROC was in SDNN which increasedby 22 ms. The individual trajectories of HF poweraround LOC and ROC were further examined fordexmedetomidine in Fig. 4 and Table 3, which showedthat for 6/9 subjects the HF power trend was consistentaround LOC and ROC. In conclusion, these pre-liminary findings suggest that the HF componentpower of HRV could be utilized in monitoring loss andrecovery of consciousness during dexmedetomidineanesthesia.
The proposed Kalman smoother spectrum estima-tion method outperformed the standard Kalmansmoother approach and STFT in identification of LOCand ROC. As the standard Kalman smootherapproach we considered the proposed method without
the spectral decomposition and respiratory frequencytracking. The respiratory frequency tracking did notactually have any effect on the identification of LOCand ROC because respiratory frequencies of all sub-jects around these time points were within the defaultHF frequency band (0.15–0.4 Hz). The respiratoryfrequency was however just below 0.15 Hz in baselinefor two subjects. The fact that the respiratorydynamics within the study population stayed mainlywithin the normal range does not reduce the impor-tance of respiratory tracking. Quite the contrary, dy-namic adjustment of the HF band according torespiratory dynamics is an important point whendeveloping time-varying HRV analysis methods thatneed to be reliable and need to operate in real timeautomatically.
Spectral decomposition, on the other hand, had anoticeable effect on the HF power estimates in fewsubjects. The two evident causes for this are; (1) astrong LF component spreads partly into the HF band(Fig. 4, S7) and (2) the respiratory frequency is close toeither of the HF band limits, and therefore part of theHF component power spreads outside the HF band(Fig. 4, S5). In the first case, the HF power will beoverestimated, and in the second case underestimated,if spectral decomposition is not used in HF powercomputation. The disadvantage of the STFT is therelatively low time-frequency resolution compared tothe Kalman smoother approach. Therefore, STFTcould not identify LOC and ROC as reliably as theproposed method.
There are several previous studies showing anoverall decrease in HRV during propofol anesthe-sia.16,20,21,27,33,35,41 Such a reduction was not observedin the present study, which is probably because thedepth of anesthesia was low and it lasted only for ashort time, whereas in most of the previous studieshigher concentrations of anesthetics (i.e., deeper anes-thesia) were used. It should also be noted that in manyprevious studies subjects have been mechanically ven-tilated which was not the case in this study. Mechan-ical ventilation can have influence on HRV because itproduces a non-physiological condition in which pul-monary pressures are reversed compared to sponta-neous breathing, and therefore normal RSA may bedisturbed.
HRV changes related to dexmedetomidine inducedanesthesia have not been studied as widely. In Penttilaet al.,32 intravenous infusion of dexmedetomidine wasfollowed by a decrease in heart rate and increase in HFvariability with maximal effect at 10 min. In Hogueet al.,18 infusion of dexmedetomidine was followed bya decrease in heart rate and LF power in unanesthe-sized subjects, but no change was observed in HFpower. In the present study, heart rate decreased and
Heart Rate Variability Dynamics 1811
HF power increased after the infusion until LOCwhich supports the results of Penttila et al.32 Fur-thermore, in Feld et al.,14 a decrease in LF and HFpowers accompanied by decrease in LF/HF ratio wasobserved during dexmedetomidine anesthesia. Ourresults did not show any clear decrease in LF or HFpower after LOC, but this is probably due to the rel-atively short lasting anesthesia in the present study.
The limitations of this study include the rather smallnumber of subjects. For anesthesia monitoring pur-poses during clinical practice, the methods should befurther tested with more subjects and also in differentexperimental conditions (deeper anesthesia, ventilatedsubjects etc.). In addition, the rather high inter-indi-vidual variability in the HRV parameters (evenbetween the healthy young male subjects tested in thisstudy) is a clear limitation. The Kalman smootheralgorithm applied in this paper was based on a linearmodel, but it would be interesting to further investigateif nonlinear modeling of the RSA could improve theidentification of LOC and ROC. Finally, the Kalmansmoother algorithm applied in this paper is not suit-able for real-time monitoring, but a fixed-lag smootheralgorithm could be used for real-time functionality.
ACKNOWLEDGMENTS
This study was supported by Academy of Finland(Project No. 126873, 1.1.2009–31.12.2011; Project No.123579, 1.1.2008-31.12.2011; and Project No. 8111818,1.1.2006–31.12.2009) and by the SalWe ResearchProgram for Mind and Body (Tekes—the FinnishFunding Agency for Technology and InnovationGrant 1104/10).
CONFLICTS OF INTEREST
None.
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