Heart Rate Variability analysis in patients undergoing local anesthesia

349 Pages·2017·23.18 MB·English

by  Neil Stewart

City Research Online
City, University of London Institutional Repository
Citation: Shafqat, K. (2010). Heart Rate Variability analysis in patients undergoing local
anesthesia. (Unpublished Doctoral thesis, City, University of London)
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Feb 2010
Abstract
TheanalysisofHeartRateVariability(HRV),thebeattobeatfluctuationintheheartrate,
isanon-invasivetechniquewithamainaimingaininginformationabouttheautonomic
neuralregulationoftheheart. AssessmentofHRVhasbeenshowntoaidclinicaldiagno-
sisandinterventionstrategies. However,therearequiteafewconflictingreportsonHRV
that perhaps impede its use as a reliable clinical tool. The complex nature of different
mechanisms that affect the HRV and the large number of signal processing techniques
thathavebeenusedforHRVanalysisarethecontributingfactorsoftheseconflictingre-
sults. TheaimofthisstudywastoinvestigateforthefirsttimetheeffectofHRVduring
Brachial plexus block (local anaesthesia), applied using the axillary approach. The hy-
pothesiswasthat,suchinvestigationwillenablethedetectionofpossiblechangesinthe
dynamicsofthecardiovascularsystemduetotheintravenousintroductionofanaesthetic
drugsduringlocalanaesthesia. ForthispurposeadvancedHRVsignalsprocessingtech-
niques were developed and evaluated on data collected before and after the application
oftheBrachialplexusblockfromfourteenpatientsundergoinglocalanaesthesia. Signal
processingtechniquesforR-wavedetection,signalrepresentation,ectopicbeatdetection
anddetrendingwerefirstdevelopedandvalidatedwiththehelpofsimulatedsignalsand
physiologicalsignalsfromPhysionetdatabase. Afterthevalidationstagethesemethods
werethenusedtoanalysethedatafromthelocallyanaesthetisedpatients.
TheECGR-wavepeakdetectionwascarriedoutusingthewavelettransformwithfirst
derivative of Gaussian smoothing function as the mother wavelet. The algorithm achie-
vedaccuracyandsensitivityofover90%. ThehearttimingsignalwasusedfortheHRV
signal representation and also for the correction of missing and/or ectopic beats. The
results obtained from the ectopic beat correction algorithm showed that the algorithm
managed to significantly reduce the error caused by missing and/or ectopic beats. De-
trending of the HRV signal was carried out using the wavelet packet analysis algorithm
which was specifically developed for this study. The respiration signal was also estima-
i
Abstract
ted from the ECG signal using the ECG Derived Respiration (EDR) technique. In order
to take better account of slow respiration rates and/or irregular respiratory patterns in
the HRV analysis, a new method for the estimation of the variable boundaries associa-
ted with the LF and the HF band of the HRV signal was implemented. This method
relies on the frequency contents of both the HRV signal and the respiration signal and
uses the cross-spectrum between these two signals to obtain the boundaries related to
theHFbandofthesignal. TheboundariesrelatedtotheLFbandweredefinedusingthe
HRVsignalspectrumalone. Theboundaryestimationtechniquewasapplicableinallthe
spectralanalysismethodsthatwereusedinthisstudy.
Afterthepre-processingstepstheclinicaldatawasanalysedusingfrequencyandtime-
frequencyanalysismethodstoobtaintheparametersrelatedtotheHRVsignals. Initially
spectralanalysiswascarriedoutusingthetraditionalnon-parametric(Welch’speriodo-
gram) and parametric (Autoregressive modelling) methods. Statistical analysis of the
parametersobtainedfromboththenon-parametricandtheparametricmethodsshowed
significantdecreaseintheLF/HFratiovalueswithinanhourofapplicationoftheblock
in nine out of fourteen patients. In order to overcome the inability of these methods to
deal with non-stationary, time-frequency analysis techniques were used to further ana-
lyse the HRV signals. The three time-frequency analysis methods used were the Conti-
nuousWaveletTransform(CWT),theWigner-VilleDistribution(SPWVD)andtheEmpi-
rical Mode Decomposition (EMD). The analysis of the parameters estimated from these
threetechniquesontheclinicaldatashowedthattheCWTandtheEMDtechniqueshave
performed equivalently, meaning that both these methods have detected significant de-
creaseinthirteenoutoffourteenpatientsfortheratiovaluesaftertheapplicationofthe
anaesthetic block. The presence of interference terms has caused the degradation in the
performance of the SPWVD method and due to this reason it was only able to detect si-
gnificant changes in the LF/HF ratio values in ten of the fourteen patients. The results
suggest that due to anxiety and/or adrenaline present in the local anaesthetic mixture,
the LF/HF ratio values showed a transient increase shortly after the application of the
block. Afterthistransientincreasetheratiovaluesdecreasedconsiderablyandremained
lowascomparedtothevaluesbeforetheapplicationoftheblock. Thisdecreasecouldre-
present the shift of the sympathovagal balance towards parasympathetic predominance
and/or inhabitation of sympathetic activity due to local anaesthesia. The use of time-
frequency analysis such as EMD and CWT could provide useful information about the
changes caused in the dynamics of the cardiovascular system when a local anaesthetic
drugisadministeredinapatient.
ii
Dedication
Dedicated to my grandparents
iii
Acknowledgements
IwouldlikeexpressmygratitudetoDr. P. A. KyriacouandDr. S. K. Palforproviding
me the opportunity to this research. I would also like to thanks Dr. P. Weller and Dr.
P. Liatsis for the useful discussions and the guidance they provided throughout my
research. IwouldalsoliketoacknowledgeDr. S. Kumariforherassistanceintheclinical
trials.
Iwouldliketosayaespecialthankstomyfamilyandmywifefortheiruncompromi-
singsupportandunderstandingduringmystudy.
Finally, IwouldliketothankmyfriendsMr. V.O.RybnokandMiss. M.Hickeywho
made it possible for me to survive the lab environment and help me in every aspect of
myresearch.
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Contents
Abstract i
Acknowledgements iv
Contents v
ListofFigures xi
ListofTables xx
Abbreviations xxii
1 Thesisoutline 1
2 Anatomyandphysiology: cardiovascularandautonomicnervoussystem 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Cardiovascularsystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.1 Theanatomyandfunctionofthehumanheart . . . . . . . . . . . . 7
2.3 Theconductionpath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Theelectrocardiogram(ECG)signal . . . . . . . . . . . . . . . . . . . . . . 10
2.4.1 ECGleadconfigurations . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5 Cardiacdisease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5.1 Myocardialischaemia . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5.2 Cardiacarrhythmias . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.6 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Physiologyofbeat-to-beatHeartRateandHeartRateVariability 17
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2 Theautonomicnervoussystemandthesympathovagalbalance . . . . . . 18
3.2.1 Reflexescontrollingheartrateanditsvariability . . . . . . . . . . . 21
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Contents
3.3 Factorsinfluencingheartrateanditsvariability . . . . . . . . . . . . . . . 22
3.3.1 Intra-patientfactors . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.2 Inter-patientfactors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 QuantifyingHRV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4.1 HRVmetricsfromtheRRtachogram . . . . . . . . . . . . . . . . . . 25
3.4.1.1 Geometricindices . . . . . . . . . . . . . . . . . . . . . . . 25
3.4.1.2 Statisticalindices . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4.1.3 Frequencydomainmetrics . . . . . . . . . . . . . . . . . . 27
3.5 TheclinicalutilityofHeartRateVariability . . . . . . . . . . . . . . . . . . 32
3.6 Standardterminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.7 Measurementstandards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.8 ClinicalapplicationsofHRV . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.9 TheproblemofHRVmeasurementandrepeatability . . . . . . . . . . . . 35
3.10 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4 Localanaesthesia 38
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2 Sideeffects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.1 Centralnervoussystemcomplications . . . . . . . . . . . . . . . . . 38
4.2.2 Cardiovascularcomplications . . . . . . . . . . . . . . . . . . . . . . 39
4.3 BrachialPlexusblock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.4 Subjectsandprotocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5 DevelopmentofR-wavedetectionalgorithmusingdigitalfilteralgorithm 44
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 ImplementationofR-wavedetectionalgorithmusingadigitalfilter . . . . 45
5.2.1 Adaptivethreshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.3 Algorithmevaluationinthepresenceofsimulatedartefacts. . . . . . . . . 47
5.3.1 WhiteNoise(WN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.3.2 ColourNoise(CN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.3.3 BaselineWander/breathingartefact(BW) . . . . . . . . . . . . . . . 50
5.3.4 MuscleArtefact(MA) . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.3.5 ElectrodeMovementArtefact(EMA) . . . . . . . . . . . . . . . . . 52
5.3.6 Combinedartefact . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.4 Off-lineevaluationofthedigitalfilterpeakdetectionalgorithmevaluation
onrealdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.5 On-lineevaluationofthedigitalfilterpeakdetectionalgorithmonrealdata 57
5.6 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
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Contents
6 DevelopmentofR-wavedetectionalgorithmusingWaveletTransform 60
6.1 Introductiontoclassicalfrequencydomainanalysismethods . . . . . . . . 60
6.2 Waveletanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.2.1 ContinuousWaveletTransform(CWT) . . . . . . . . . . . . . . . . 63
6.2.2 DiscreteWaveletTransform(DWT). . . . . . . . . . . . . . . . . . . 65
6.2.2.1 Scalingfunction . . . . . . . . . . . . . . . . . . . . . . . . 67
6.2.2.2 FastWaveletTransform . . . . . . . . . . . . . . . . . . . . 68
6.3 ImplementationofECGcharacterisationalgorithm . . . . . . . . . . . . . 69
6.3.1 R-waveandQRSonset/offsetdetection . . . . . . . . . . . . . . . . 71
6.4 Algorithmevaluationinpresenceofsimulatedartefacts . . . . . . . . . . . 74
6.4.1 WhiteNoise(WN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.4.2 ColourNoise(CN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.4.3 BaselineWander/breathingartefact(BW) . . . . . . . . . . . . . . . 76
6.4.4 MuscleArtefact(MA) . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.4.5 ElectrodeMovementArtefact(EMA) . . . . . . . . . . . . . . . . . 79
6.4.6 Combinedartefact . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.5 Off-lineevaluationofthewaveletpeakdetectionalgorithmevaluationon
realdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.7 ConclusionsfromECGR-wavedetection . . . . . . . . . . . . . . . . . . . 84
7 Introduction: SpectralAnalysis 86
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
7.2 Applicationofnon-parametricmethodsfortheestimationofPSD . . . . . 87
7.3 ApplicationofparametricmethodsforthePSDestimation . . . . . . . . . 89
7.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
7.3.2 AutoregressiveSpectralestimation . . . . . . . . . . . . . . . . . . . 89
7.3.2.1 Modelorderselection . . . . . . . . . . . . . . . . . . . . . 91
7.4 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
8 TachogramresamplingandEctopicbeatcorrection 93
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.2 LinearandSplineInterpolation . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.3 HeartratesignalusingBergeralgorithm . . . . . . . . . . . . . . . . . . . . 95
8.4 Ectopicandmissingbeatcorrection . . . . . . . . . . . . . . . . . . . . . . 97
8.4.1 Ectopicbeatdetectionandcorrectionalgorithm . . . . . . . . . . . 98
8.5 Effectofectopicbeatonsimulatedsignals . . . . . . . . . . . . . . . . . . . 103
8.5.1 Simulatedsignalresultsusingnon-parametricmethod . . . . . . . 103
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Contents
8.5.2 SimulatedSignalResultsusingparametricmethod . . . . . . . . . 109
8.6 EffectofectopicbeatonrealHRVsignal . . . . . . . . . . . . . . . . . . . . 113
8.7 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
9 Tachogramdetrending 119
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
9.2 DetrendingusingSmoothnessPriorApproach(SPA) . . . . . . . . . . . . 120
9.2.1 DetrendingresultsusingSmoothnessPriorApproach(SPA) . . . . 122
9.3 DetrendingusingWaveletPackets . . . . . . . . . . . . . . . . . . . . . . . 125
9.3.1 WaveletPackets(WP)Analysis . . . . . . . . . . . . . . . . . . . . . 126
9.3.2 DetrendingresultsusingWaveletPacket(WP)analysis . . . . . . . 127
9.4 Parametercomparisonbetweentwodetrendingtechniques . . . . . . . . . 130
9.5 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
10 Respirationsignalestimation 135
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
10.2 EDRalgorithmdescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
10.3 EvaluationoftheimplementedEDRalgorithm . . . . . . . . . . . . . . . . 137
10.4 Summaryandconclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
11 Parametricandnon-parametricfrequencyanalysisofHRVdatafromlocallyanaes-
thetisedpatients 144
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
11.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
11.2.1 FrequencydomainHRVparameterestimation . . . . . . . . . . . . 145
11.2.2 Resultsofnon-parametricanalysisoflocallyanaesthetisedpatients’
data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
11.2.3 Resultsofparametricanalysisoflocallyanaesthetisedpatientsdata 151
11.3 Statistical test for non-parametric and parametric analysis of HRV data
fromlocallyanaesthetisedpatients . . . . . . . . . . . . . . . . . . . . . . . 154
11.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
12 Wavelet(WT)analysisofHRVdatafromlocallyanaesthetisedpatients 162
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
12.2 ResultsofsimulatedsignalsanalysiswithCWT . . . . . . . . . . . . . . . 163
12.2.1 Simulatedsignal: 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
12.2.2 Simulatedsignal: 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
12.2.3 Simulatedsignal: 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
12.2.4 Simulatedsignal: 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
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