Quantification of haemodynamic response to auditory stimulus in intensive care

Computer Methods and Programs in Biomedicine 63 (2000) 211–218

Quantification of haemodynamic response to auditorystimulus in intensive care

Ilkka Korhonen a,*, Jari Karhu b, Luca Mainardi c, Stephan M. Jakob d

a VTT Information Technology, P.O BOX 1206, FIN-33101 Tampere, Finlandb Kuopio Uni6ersity Hospital, Department of Clinical Neurophysiology, P.O. BOX 1777, FIN-70211 Kuopio, Finland

c Polytechnic Uni6ersity of Milan, Department of Biomedical Engineering, Piazza Leonardo da Vinci 32, 20133 Milano, Italyd Kuopio Uni6ersity Hospital, Department of Intensi6e Care, P.O. BOX 1777, FIN-70211 Kuopio, Finland

Received 18 October 1999; received in revised form 11 February 2000; accepted 29 February 2000

Abstract

Measuring effects of sensory stimuli on haemodynamics could provide information about the interplay betweencentral and autonomous nervous system (ANS). However, ANS response to sensory stimulus has received littleattention. In this paper we present a signal processing scheme to extract the responses of heart rate and systemicarterial pressure on auditory stimulus in intensive care patients (N=5). In short, the effect of mechanical ventilationis rejected by optimal linear modelling. Other disturbances are attenuated by filtering and efficient rejection ofoutlying sweeps of data. The results show identifiable responses on three out of five cases. The response characteristicsmay be explained by synchronisation of spontaneous variability in systemic arterial pressure to auditory stimulus.© 2000 Elsevier Science Ireland Ltd. All rights reserved.

Keywords: Autonomous nervous system; Haemodynamic response; Auditory stimulus; Heart rate variability

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1. Introduction

Beat-to-beat variability in heart rate and sys-temic arterial pressure (SAP) is related to thefunctioning of the autonomous nervous system(ANS). Heart rate variability (HRV) related torespiration is called respiratory sinus arrhythmia(RSA), and it is predominantly controlled by theparasympathetic nervous system [1]. The lower

frequency variability around 0.1 Hz, also referredas Mayer waves, is controlled both by sympa-thetic and parasympathetic nervous systems [2].Recently, HRV and especially RSA have beenstudied in critically ill or anaesthetised patients.Both RSA [3] and HRV [4] have been found to berelated to the level of sedation in critically illpatients. Pomfrett et al. showed a significant de-crease in the level of RSA during induction ofisoflurane anaesthesia and an increase during therecovery [5], and Sleigh and Donovan reported adecrease in HRV during induction of generalanaesthesia [6]. Similarly, Mainardi et al. reported

* Corresponding author. Tel.: +358-3-3163352; fax: +358-3-3174102.

E-mail address: [email protected] (I. Korhonen).

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I. Korhonen et al. / Computer Methods and Programs in Biomedicine 63 (2000) 211–218212

low frequency power increase during airway suc-tion in critically ill patients, indicating sympa-thetic activation during this uncomfortable oreven painful manoeuvre [7]. Hence, it has beenproposed that HRV and especially RSA may beclinically useful as a predictor of inadequateanaesthesia and/or analgesia [8].

With respect to ANS respiration may be con-sidered as a repetitive input stimulus and RSA asan output response corresponding to this stimu-lus. Potentially, measuring effects of sensory stim-uli on ANS could provide information about theinterplay between central and autonomous ner-vous systems. However, ANS response to sensorystimulus has received little attention. Scholz et al.studied the effect of transcranial magnetic stimu-lation on heart rate, and found a possible syn-chronisation of 0.1 Hz rhythm with the stimulus[9]. Haemodynamic responses to auditory stimulihave not been studied previously. The objectivesof the present study were to (1) develop signalprocessing methods to extract haemodynamic re-sponses to auditory stimulus from the measuredECG and SAP data, and (2) test the functioningof the methods on post cardiac surgery intensivecare patients sedated with propofol.

2. Materials and methods

2.1. Subjects and study protocol

Five intensive care patients (four males, onefemale) were studied during recovery from propo-fol anaesthesia after coronary artery bypasssurgery at Kuopio University Hospital. Exclusioncriteria were clinical signs of heart failure, dia-betes mellitus, arterial fibrillation, or severearrhythmia during the study. Written informedconsent was obtained from all patients. The pa-tients form a randomly selected sub-group from alarger cohort, which has been described in detailelsewhere [10]. The patients were studied rightafter the arrival to the intensive care unit whenthey were deeply sedated with propofol (Ramseyscore 6–4).

Trains of four auditory stimuli (inter-stimulusinterval 1000 ms) were introduced to the right ear

with 800 Hz sine wave (duration 85 ms, including8.5 ms rise and fall time) at 60 dB above normalhearing level. The patients’ hearing levels were notspecifically tested, but patients with a history ofhearing problems or difficulties in understandingnormal talking were excluded. The inter-train in-terval was 12 s. ECG (sampling rate 250 Hz), SAP(50 Hz) and airway pressure (25 Hz) were mea-sured together with stimulus marker channel (100Hz). The data were stored and the analysis wasperformed off-line. The patients were not dis-turbed during the procedure to avoid any arte-facts in the data.

2.2. Signal processing

2.2.1. Time series extraction and signalpreprocessing

QRS complex detection from ECG was auto-matically performed by a derivative/threshold al-gorithm [11] looking at two ECG leadssimultaneously. The first-slope of the QRS com-plex was chosen as fiducial QRS point. R-peaklocalisation was obtained through a parabolic in-terpolation and detected as the apex of the inter-polating parabolas. The RR-interval (RRI) wascomputed as the distance between the maximumof two successive interpolating curves. Interpola-tion reduces the jitters effect induced by signalsampling and improve R-peak localisation [12].The accuracy in R-peak detection was visuallychecked and corrected by the operator using com-mercially available software (Cardioline RemcoItalia, AD35 Top).

Extraction of SAP time series was performedusing the R-peak as a reference. The systolic SAP(SAPs) was detected as the maximum pressureduring the first 80% of the next RRI, and thediastolic SAP (SAPd) was detected as the mini-mum pressure between the detected systolicinstants.

After beat-to-beat series extraction, the timeseries were linearly interpolated and re-sampled at12.5 Hz. Relatively high re-sampling frequencycompared to normal heart frequency (around 1Hz) was necessary to make it possible to accu-rately synchronise the auditory stimulus andhaemodynamic responses. In the interpolation

I. Korhonen et al. / Computer Methods and Programs in Biomedicine 63 (2000) 211–218 213

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procedure, the actual instant of occurrence wasused for the SAPs and SAPd. For an RRI definedby two consecutive R-peaks R(i ) and R(i+1), thetime instant of R(i+1) was used as the instant ofoccurrence. The airway pressure signal was low-pass filtered and down-sampled by 2 to match itwith the RRI and SAPs,d.

2.2.2. Cancellation of non-coherent 6ariabilityIn order to enhance the haemodynamic response

to auditory stimulus, multistage linear filtering wasadopted to suppress any other sources of variationin cardiovascular signals (Fig. 1).

Any slow baseline variations were estimated withlinear filtering of the signal with 60-s Hanningwindow finite impulse response filter. The estimatedtrend was subtracted from the signal. Essentially,this corresponds to high-pass filtering (cut-off 0.02Hz).

RRI increases during inspirium and decreasesduring expirium with positive pressure ventilation[13]. The same relationship holds true with SAP.

When studying haemodynamic responses to exter-nal stimuli, this variability related to ventilationmay be considered as an artefact, which disturbsthe analysis. We applied optimal linear filtering toattenuate this artefact from RRI and SAPs, d. Theeffect of mechanical ventilation on RRI was mod-elled as

RRI(t)= %p

i=0

b(i )AWP(t− i )+e(t) (1)

where AWP is the airway pressure signal, b are thefilter coefficients, p is the filter order, and e(t) is thatpart of RRI, which is not correlated with ventila-tion. The equation is solved in least squares senseto minimise e(t). A similar model was defined forSAPs, d. Filter order of 30 was experimentally foundto be sufficient to describe the effect of ventilationon RRI or SAPs, d. Finally, the residual e(t) waslow-pass filtered (zero-phase forward backwardfiltering with 5th order Chebychev II type filter withcut-off −3 dB at 0.63 Hz) to attenuate highfrequency noise and modelling ripple in Eq. (1). Theend result of the filtering was used in all the furtherprocessing. An example of processing is providedin Fig. 2.

2.2.3. Rejection of outlying sweeps and a6eragingSweeps of RRI and SAPs, d were extracted in

accordance with the auditory stimuli (−1–16 s inrespect to first stimulus in each train). To improvesignal-to-noise ratio of the average response, outly-ing sweeps were detected and rejected from furtheranalysis. Prior to analysis, any remaining baselinevalue of each sweep was removed by subtracting thesignal value at time t=0 (time of the first stimulusin the train) from the sweep. Furthermore, theresponse for the very first stimulus train wasexcluded. A reference sweep waveform was derivedby

xref(t)=median[xi(t)] i=1...number of sweeps,

(2)

The median waveform was used instead of mean asit is less biased by severe artefacts, which arecommon in RRI or and SAPs, d signals, e.g. due toarrhythmia or technical disturbances. Then, a dis-tance of each sweep xi from the reference xref wascomputed:

Fig. 2. Example of filtering. Panels from top to bottom: (1)original SAPs, (2) SAPs without a baseline drift, (3) respiratorycomponent of the SAPs (RSAP), (4) non-respiratory compo-nent of the SAPs (NRSAP), and (5) smoothed non-respiratorySAPs. The time instants of auditory stimulation are indicatedon the lowest panel. Note efficient cancellation of ventilationrelated variability. Unit in all panels is mmHg.


Fig. 3. Example of improvement of the responses after cancellation of ventilation artefact. Left hand side: average responses withno ventilation artefact cancellation. Right hand side: same data after removal of ventilation artefact. Note especially theimprovement of SAP responses. Rhythmic variations in RRI on the left hand side are due to partial synchrony of the ventilationon auditory stimuli.

D2, i=' 1

N%N

t=1

[xi(t)−xref(t)]2 (3)

where N is the number of samples in the sweep.Theoretically, D2

2 is x2-distributed, and for N\30, D2 is approximately normally distributed [14].Hence, an outlier xk was declared if

xk=max{D2, i �D2, i\csD 2+mD 2

} (4)

where sD 2is the S.D. of all the distances D2, i, mD 2

is their mean, and c is a threshold for rejection. Ifan outlier was identified, it was removed from thesweep set and Eqs. (2)–(4) were recomputed untilno more outliers were found. In practice, c=2was used corresponding to a theoretical probabil-ity value of P=0.05 for rejection of a validsweep. The remaining sweeps were then averagedto derive the response waveform.

2.3. Data analysis

Subjective visual analysis of the waveform data

was carried out to identify the presence of re-sponses. In those subjects, who showed an iden-tifiable response, the response amplitude andlatency were quantified as the amplitude and la-tency of the main peak in the response.

3. Results

The effect of ventilation was efficiently can-celled in all the cases as demonstrated in Figs. 2and 3 (visual judgement). Rejection of outlyingsweeps led to rejection of 11–39% of the sweeps(Fig. 4). Three out of five cases (c001, c010and c16) showed clear RRI and/or SAP re-sponses to auditory stimuli (Fig. 5). An increasein SAP (both in systolic and diastolic) was seen inall these cases with peak latency from 2.7 to 6.7 s(Table 1). In two subjects (c001 and c016) theSAP increase was accompanied with a syn-chronous heart rate response. In the subject


c001, the heart rate was accelerated with a simi-lar latency to SAP. The subject c016 showed arapid deceleration of heart rate, which was syn-chronous to increased SAP. This deceleration wasfollowed by a slower acceleration with latencysimilar to subject c001; however, without amarked slower increase in SAP. In the remainingtwo cases (c006 and c011) SAP showed asimilar response waveform, which was howeverconsidered as not significant response due to smallamplitude compared to noise.

4. Discussion

We presented a signal processing scheme, whichenabled to detect responses in RRI and SAP toauditory stimuli despite strong interference caused

by mechanical ventilation and artefacts, which arecommon in these signals in intensive care patients.The central components of the scheme were can-cellation of ventilation artefact and rejection ofoutlying sweeps before computing the averageresponse.

The method for cancellation of ventilation arte-fact was comparable to that proposed by Scholzet al. [9], who used adaptive filtering to cancel theeffect of spontaneous respiration on RRI. In ourstudy, the patients were mechanically ventilatedand deeply sedated, and the physiological situa-tion as well as ventilation parameters were keptstationary during the stimulation. Hence, weadopted high-order time-invariant model and notadaptive filtering. The high model order (30) wasfound to be required to efficiently model the effectof mechanical ventilation on RRI or SAP. This isprobably due to highly non-sinusoidal ventilationwaveform, which requires large number of coeffi-cients in order to be modelled by linear models.The results showed that the model was capable toestimate the effect of mechanical ventilation onRRI and SAP, and by using the model we couldsignificantly improve the average response wave-forms (see Fig. 3).

We found that efficient rejection of outlyingsweeps from the data is a prerequisite for success-ful analysis of haemodynamic responses to audi-tory stimulus. Typical outlying sweeps in RRI orSAP are caused by arrhythmias or technical arte-facts, which cause very large deviations from thebaseline (hundreds of ms for RRI, tens of mmHgfor SAP) compared to responses to auditory stim-ulus (few ms or mmHg) (Fig. 4). The medianoperator instead of mean in Eq. (2) enables robustidentification of a likely response waveform dur-ing the outlier rejection process in the presence ofmultiple such artefacts. As an end result, 11–39%of the sweeps needed to be rejected from theanalysis.

A presence of a haemodynamic response couldbe identified in three out of five subjects. In theremaining two subjects the response was eithermissing or so weak that it could not be identifiedin the presence of other variability and distur-bances. This may be partially due to the fact thatthe subjects were just recovering from cardiac

Fig. 4. An example of sweep processing. All the sweeps (thinlines) are shown on the topmost panel after rejection of thesweep baseline. Rejected sweeps (thin lines) and the average ofthe accepted sweeps (thick line) are shown on the middlepanel. The lowest panel shows the average waveform (thickline) vs. all the accepted sweeps (thin lines). The exampleillustrates efficient rejection of outlying sweeps.


Fig. 5. RRI and SAP responses all subjects. Dashed lines indicate 95% confidence intervals. Ratio of accepted-to-rejected sweeps isindicated for each response. Three subjects (c001, c010 and c016) showed an identifiable response. Typical response is anincrease in SAP (both systolic and diastolic). In subjects c001 and c016 this is accompanied with an acceleration of hear rate(shortening of RRI). Peak latency is around 5–6 s in cases c001 and c010, and around 3 s in case c016. The responses on thecases c006 and c011 were considered as not significant.

bypass surgery and anaesthesia, and were deeplysedated, which has an effect on the ANS function-ing and responses.

Though the response waveform data weresomewhat controversial, the general pattern oftime locked ANS reaction to auditory stimuli isthat of a predominantly sympathetic response.The time course of RRI and SAP responses alsoclosely resembles the time course of a skin con-duction response, which is traditionally used as a

marker of central nervous system–ANS interac-tion during subject’s automatic responses to novelor salient stimuli from the surroundings. The pat-tern of the response identified in this study inthree out of five cases is in agreement with thestudy by Scholz et al. who recorded RRI re-sponses on transcranial magnetic stimulation [9].They suggested that the spontaneous low fre-quency oscillations in RRI around 0.1 Hz couldbe synchronised to the stimuli. This phenomenon


Table 1Response amplitudes and latencies from subjects with iden-tifiable response

Signal AmplitudeCase c Latency (s)(ms/mmHg)

−3.0RRI 4.7110 – –

3.5 1.516

1.6SAPs 6.710.8 5.2100.5 2.716

SAPd 1 1.3 5.20.610 5.8

16 0.8 2.8

the North American Society of Pacing and Electrophysi-ology, Heart rate variability: standards of measurement,physiological interpretation and clinical use, Circulation93 (1996) 1043–1065 Special report.

[2] D.L. Eckberg, Sympathovagal balance. A critical ap-praisal, Circulation 96 (1997) 3224–3232.

[3] D.Y. Wang, C.J.D. Pomfrett, T.E.J. Healy, Respiratorysinus arrhythmia: a new, objective sedation score, Br. J.Anaesth. 71 (1993) 354–358.

[4] C. Haberthur, F. Lehman, R. Ritz, Assessment of depthof midazolam sedation using objective parameters, Inten-sive Care Med. 22 (1996) 1385–1390.

[5] C.J.D. Pomfrett, J.R. Sneyd, J.R. Barrie, T.E.J. Healy,Respiratory sinus arrhythmia: comparison with EEG in-dices during isoflurane anaesthesia at 0.65 and 1.2 MAC,Br. J. Anaesth. 72 (1994) 397–402.

[6] J.W. Sleigh, J. Donovan, Comparison of bispectral index,95% spectral edge frequency and approximate entropy ofthe EEG, with changes in heart rate variability duringinduction of general anaesthesia, Br. J. Anaesth. 82 (5)(1999) 666–671.

[7] L.T. Mainardi, A. Yli-Hankala, I. Korhonen, M.G. Sig-norini, J. Takala, K. Nieminen, S. Cerutti, Monitoringthe autonomic nervous system in the ICU through cardio-vascular variability signals, IEEE. Eng. Med. Biol. 16 (6)(1997) 64–75.

[8] C.J.D. Pomfrett, Editorial I. Heart rate variability, BISand ‘depth of anaesthesia’, Br. J. Anaesth. 82 (5) (1999)659–661.

[9] U.J. Scholz, L. Niehaus, L.C.G. Hahn, A.M. Bianchi,B.-U. Meyer, Autonomic response to transcranial mag-netic stimulation: adaptive filtering of the heart rate vari-ability, in: Medicon ‘98 CD-ROM, Lemesos, Cyprus,June 14–17, 1998.

[10] S.M. Jakob, K. Nieminen, J. Hiltunen, J. Karhu, J.Takala, IBIS data library: clinical description of theFinnish data base, Comput. Methods Prog. Biomed. 63(2000) 161–166.

[11] G. Baselli, S. Cerutti, Identification techniques applied toprocessing of signals from cardiovascular system, Med.Inform. 10 (1985) 223–235.

[12] M. Merri, D.C. Farden, J.C. Mottley, E.L. Titlebaum,Sampling frequency of the electrogardiogram for spectralanalysis of the heart rate variability, IEEE-TBME 37(1990) 99–106.

[13] A. Yli-Hankala, T. Porkkala, S. Kaukinen, V. Hakkinen,V. Jantti, Respiratory sinus arrhythmia is reversed duringpositive pressure ventilation, Acta Physiol. Scand. 141(1991) 399–407.

[14] L. Rade, B. Westergren, Beta Mathematics Handbook,Studentlitteratur, Lund, Sweden, 1988, p. 336.

[15] R.I. Kitney, T. Fulton, A.H. McDonald, D.A. Linkens,Transient interactions between blood pressure, respirationand heart rate in man, J. Biomed. Eng. 7 (1985) 217–224.

resembles frequency pulling and it has usuallybeen associated with respiration at frequenciesclose to 0.1 Hz [15]. The alteration of SAP ob-served in this study may also be associated withre-phasing the existing spontaneous variability.The interval between the start of each stimulustrain in our study was 15 s (0.067 Hz), which maybe close enough to entrain with the spontaneous10-s rhythm. Further studies with more subjectsare clearly needed to clarify the physiologicalmechanism underlying this sympathetic response.Clinically, the significant blood pressure and heartrate changes in stabilised patients after heartsurgery need to be associated with the level ofsedation and medication as well as with otherclinical data. Sudden sensory stimuli are unavoid-able in operating areas and in intensive care units,and may, according to present results, cause func-tional changes in the cardiovascular system.

Acknowledgements

The research was supported by funding fromthe European Commission under contract BMH4-97-2570.

References

[1] Task Force of the European Society of Cardiology and

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Quantification of haemodynamic response to auditory stimulus in intensive care