Poincaré Plot Methods for Heart Rate Variability Analysis

158 Pages·2013·3.09 MB·English

by  Ahsan Habib Khandoker

Ahsan Habib Khandoker
Chandan Karmakar
Michael Brennan
Andreas Voss
Marimuthu Palaniswami
Poincaré Plot
Methods for Heart
Rate Variability
Analysis
Poincare´ Plot Methods for Heart Rate Variability
Analysis

Ahsan Habib Khandoker (cid:129) Chandan Karmakar
Michael Brennan (cid:129) Andreas Voss
Marimuthu Palaniswami
Poincare´ Plot Methods
for Heart Rate Variability
Analysis
123
AhsanHabibKhandoker ChandanKarmakar
DepartmentofBiomedicalEngineering ElectricalandElectronicEngineering
KhalifaUniversity,AbuDhabi,UAE TheUniversityofMelbourne
Melbourne,VIC,Australia
DepartmentofElectricalandElectronic
Engineering
AndreasVoss
TheUniversityofMelbourne,VIC,Australia
DepartmentofMedicalEngineering
andBiotechnology
MichaelBrennan
UniversityofAppliedSciencesJena
ElectricalandElectronicEngineering
Jena,Germany
TheUniversityofMelbourne
Melbourne,VIC,Australia
MarimuthuPalaniswami
ElectricalandElectronicEngineering
TheUniversityofMelbourne
Melbourne,VIC,Australia
ISBN978-1-4614-7374-9 ISBN978-1-4614-7375-6(eBook)
DOI10.1007/978-1-4614-7375-6
SpringerNewYorkHeidelbergDordrechtLondon
LibraryofCongressControlNumber:2013939585
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Preface
Heart rate variability is the study of autonomic nervous activity through the
informationprovidedbyfluctuationsinheartrate.Autonomicactivityisresponsible
for regulating heart rate. By studying the beat-to-beat variability in the intervals
between heartbeats, it is possible to form a representation of autonomic nervous
activity.Alterationsinthisactivitycanthusbequantitativelymeasuredwithanon-
invasive technique. Accordingly, heart rate variability has become a very active
field of research and the most popular biological signal by which the autonomic
nervoussystem is studied. Heart rate variability can measurethe individuallevels
of parasympathetic and sympathetic modulation of heart rate, and from this
informationonecanmakepredictionsonthestateoftheautonomicnervoussystem.
Moreover,adeeperexplorationofthephysiologicalbasisoftheautonomicnervous
systemcanbeinvestigated.
The study of the autonomic nervous system from the information contained
in heart rate relies upon mathematical models and techniques and the power
of digital computers to achieve reliable and accurate results. In the past only
simplistic mathematical techniques have been brought to bear due to the limited
abilityofclinicalinvestigatorsin thisregard.Recently,mathematicians,physicists
and engineers have worked alongside cardiologists and physiologists to develop
sophisticated models and mathematical techniques for the analysis of heart rate
data.Thedevelopmentofmoreaccuratemodelsofheartratevariabilityandrobust
analysistechniquesthatareimmunetothelargelevelsofnoiseandartefactfound
in all biological signals is an active field of research currently. Advances in this
areahavedirectbenefitstoclinicalandphysiologicalstudiesthatemployheartrate
variabilitytostudydiseaseandphysiology.
Many of the mathematical techniques are only approximations and have de-
fects that are not obvious except when analysed carefully with a mathematical
formulation. Mathematical analysis allows one to investigate the limitations of
analysis techniques. Further, the full potential of the analysis techniques is often
only revealed by a full mathematical treatment. The theoretical study of models
of the autonomic system has similar characteristics, with the limitations and full
descriptivepowerofamodelbeinglargelyunknownuntilstudiedmathematically.
vii
viii Preface
The study of models also allows the developmentof optimal analysis techniques.
ManyofthemathematicalalgorithmsandmodelsappliedinHRV analysisremain
uninvestigated.Theproblemis, howdowe quantitativelycharacterize(linearand/
ornonlinear)theheartratetimeseriestocaptureusefulsummarydescriptionsthat
are independentof existing HRV measures? Recent research on HRV has proven
that Poincare´ plot analysis (PPA) is a powerfultool to mark short-term and long-
term HRV. Researchers have investigated a number of techniques: converting the
two-dimensionalplot into various one-dimensionalviews; the fitting of an ellipse
to the plot shape; and measuring the correlation coefficient of the plot. In fact,
they are all measuring linear aspects of the intervals which existing HRV indices
already specify. The fact that these methods appear insensitive to the nonlinear
characteristicsofthe intervalsis an importantfindingbecausethe Poincare´ plotis
primarilyanonlineartechnique.Thisresultmotivatesthesearchforbettermethods
forPoincare´plotquantification.Thisprovidesthemotivationforthisbook.
Chapter 1 gives an overview of the physiological concepts and necessary
backgroundin the field of heart rate variability, including the history, physiology,
analysistechniquesandthe clinicalsignificanceofthe field.Thisincludesmodels
ofheartratevariabilityandthemathematicalsignalsemployedtocharacterizeheart
ratevariability.Detailsofthetime-domainandfrequency-domainanalysisofthese
signalsarealsocovered.
Chapter 2 providesa mathematical analysis of a common heart rate variability
technique known as the Poincare´ plot. The Poincare´ plot is an emerging analysis
technique that takes a sequence of intervals between heartbeats and plots each
intervalagainstthefollowinginterval.Thegeometryofthisplothasbeenshownto
distinguishbetweenhealthyandunhealthysubjectsinclinicalsettingsbyemploying
trainedspecialiststovisuallyclassifytheplots.ThePoincare´plotisavaluableHRV
analysis technique due to its ability to display nonlinear aspects of the interval
sequence. In particular we investigate the question of whether existing measures
of Poincare´ plot geometry reflect nonlinear features of heart rate variability. We
show that methods of Poincare´ quantification that summarize the geometrical
distributionofthepointswith“moment-like”calculations,i.e.meansandstandard
deviations,etc.,areunlikelytobeindependentofexistinglinearmeasuresofheart
ratevariability.
Chapter 3 of the book investigates Poincare´ plot interpretation using a new
oscillator model of heart rate variability. This chapter develops a physiologically
plausiblemathematicalmodelofautonomicnervouscontrolofheartratebasedona
seriesofwell-studiedoscillationsinheartrate.Byemployingtheresultsdescribed,
thetimeseriesofintervalsbetweenheartbeatsareabletobeanalyticallydetermined.
ThepropertiesofthePoincare´ plotcanthenbederived.ByanalysingthePoincare´
plotintermsofanunderlyingmodelofHRV,thetheoreticalbasisofPoincare´ plot
morphologycan be precisely related back to the model and therefore back to the
physiological causes. This provides a deeper understanding of the Poincare´ plot
than has previously been possible. To validate the model, simulations of various
autonomicconditionsarecomparedtoHRVdataobtainedfromsubjectsunderthe
prescribed conditions. For a variety of autonomic balances, the model generates
Preface ix
Poincare´ plotsthatundergomorphologicalalterationsstronglyresemblingthoseof
actualheartbeatintervals.
Poincare´ plot is valuable due to its ability to display nonlinear aspects of the
datasequence.However,theproblemliesincapturingtemporalinformationofthe
plot quantitatively.The standard descriptors used in quantifyingthe Poincare´ plot
(SD1, SD2)measurethegrossvariabilityofthetimeseriesdata.Determinationof
advancedmethodsforcapturingtemporalpropertiesposesa significantchallenge.
Chapter 4 proposes a noveldescriptor “Complex Correlation Measure (CCM)” to
quantifythe temporalaspectofthePoincare´ plot.IncontrasttoSD1andSD2,the
CCMincorporatespoint-to-pointvariationofthesignal.
Theasymmetryinheartratevariabilityisa visiblyobviousphenomenoninthe
Poincare´plotofnormalsinusrhythm.Itshowstheunevennessinthedistributionof
pointsaboveandbelowthelineofidentity,whichindicatesinstantaneouschangesin
thebeat-to-beatheartrate.Themajorlimitationoftheexistingasymmetrydefinition
is that it considers only the instantaneous changes in the beat-to-beat heart rate
ratherthanthepattern(increase/decrease).Chapter5describesanoveldefinitionof
asymmetryconsideringthegeometryofa2DPoincare´plot.Basedontheproposed
definition, traditional asymmetry indices—Guzik’s index (GI), Porta’s index (PI)
andEhlers’index(EI)—havebeenredefined.
Chapter6ofthebookconsidersthesegmentedaspectsofPoincare´ plotremain-
ing,ontheonehand,thenonlinearpropertiesofthesystemand,ontheotherhand,
providinghighresolutioninformationaboutthe time course and time correlations
withinaheartratetimeseries.Thesenewapproachesweresuccessfullyintroduced
inriskstratification.
Thisbookshouldbeofconsiderablehelptoresearchers,professionalsinmedical
deviceindustries,academicsandgraduatestudentsfromawiderangeofdisciplines.
Thetextprovidesacomprehensiveaccountofrecentresearchinthisemergingfield
andweanticipatethattheconceptspresentedherewillgeneratefurtherresearchin
thisfield.
Melbourne,VIC,Australia AhsanHabibKhandoker
Melbourne,VIC,Australia ChandanKarmakar
Melbourne,VIC,Australia MichaelBrennan
Jena,Germany AndreasVoss
Melbourne,VIC,Australia MarimuthuPalaniswami