ECG Parameters for Malignant Ventricular Arrhythmias

13 Pages·2017·1.19 MB·English

by  Satria Mandala

J.Med.Biol.Eng.(2017)37:441–453
DOI10.1007/s40846-017-0281-x
REVIEW ARTICLE
ECG Parameters for Malignant Ventricular Arrhythmias:
A Comprehensive Review
Satria Mandala1 • Tham Cai Di2
Received:26April2016/Accepted:31August2016/Publishedonline:28June2017
(cid:2)TheAuthor(s)2017.Thisarticleisanopenaccesspublication
Abstract Many studies showed electrocardiogram (ECG) 1 Introduction
parameters are useful for predicting fatal ventricular
arrhythmias (VAs). However, the studies have several Malignant spontaneous ventricular arrhythmias (VAs),
shortcomings. Firstly, all studies lack of effective way to namely ventricular tachycardia (VT) and ventricular fib-
present behavior of various ECG parameters prior to the rillation (VF) can cause sudden cardiac arrest. Patients
occurrence of the VAs. Secondly, they also lack of dis- who are susceptible to VT/VF always have a risk of sud-
cussion on how to consider the parameters as abnormal. den cardiac death [1]. Therefore, prediction of VT/VF
Thirdly, the reports do not include approaches to increase prior to their initiation is vital to avoid delay of rescue
the detection accuracy for the abnormal patterns. The actions [2].
purpose of this study is to address the aforementioned Researchers has discovered that ECG signals comprise
issues. It identifies ten ECG parameters from various non-invasive parameters that could reflect underlying VT/
sources and then presents a review based on the identified VF [3–5]. The ECG parameters, such as fragmented QRS
parameters. From the review, it has been found that the (fQRS),heartratevariability(HRV),Tpeak-Tend(TpTe),
increased risk of VAs can be represented by presence and heart rate turbulence (HRT) and T wave alternans (TWA)
certain abnormal range of the parameters. The variationof have predictive value for the arrhythmic events [6–8]. In
parameters range could be influenced by either gender or this case, cardiac monitoring by analyzing the ECG
age.Thisstudyalsohasdiscoveredthefactsthataveraging, parameters is an effective way to determine future occur-
outliers elimination and morphology detection algorithms rence of the fatal VT/VF.
can contribute to the detection accuracy. InordertotrackdevelopmentoftheECGparametersas
promisingpredictors,acomprehensivereviewisnecessary.
Keywords Ventricular arrhythmia (cid:2) Prediction (cid:2) ECG A few reports, such in [3, 4] seem can be considered falls
parameter (cid:2) Behavior pattern into this area. However, they lack of effective way to
present pattern of various ECG parameters prior to the
occurrenceoftheVAs.Itishardtodistinguishthepatterns
that represent increased risk of the arrhythmias from these
detailed reviews [3, 4]. This is because of authors of the
reviewstendtointroducetheidentifiedparameterswithout
grouping their similarity in patterns prior to the
& SatriaMandala arrhythmias.
[email protected] Second, the reports are also not clearly in defining how
to consider the patterns as abnormal. For example,
1 FakultasInformatika,UniversitasTelkom,Jl.
researchers in [3] only present a hazard ratio for sudden
TelekomunikasiTerusanBuahBatu,Bandung40257,
Indonesia death due to abnormal QT prolongation, but not range of
2 IJN-UTMCardiovascularEngineeringCentre,Universiti the parameters that are considered as abnormal. In this
TeknologiMalaysia,Johor,Malaysia case,theresearchershavetofindoutcertainvaluesthatcan
123
442 S.Mandala,T.C.Di
represent the abnormality via either experiments or other iii. The parameters are mostly derived from two ECG
literatures. This process is scientifically proven time- components,namelyQRSdurationandT-wave.This
consuming. paper has identified several suggested methods from
Third, these reports have not included approaches to literatures to increase correct detection rate of the
increase detection accuracy of the parameters. The detec- parameters used in the VAs prediction.
tion accuracy is important because the abnormal patterns
This paper consists of six sections. It begins with an
may undetected if the parameters fail to be accurately
Introduction section, followed by four sections that
identified. A report that is presented in [4] stated conflict-
describe ECG parameters that can predict the life-threat-
ing results of prolonged QT interval (QT) as predictors
i eningVT/VF,andendswithaConclusionsection.Thefirst
when there are difficulties in measuring the QT correctly.
i sectionexplainsselectionofthepotentialECGparameters.
The discussed problems so far leave three important
The next two sections are inline with the identified three
questions unanswered:
problems.And,thefourthsectionprovidesadiscussionon
i. How to effectively present the patterns of the ECG theselectedtenECGparametersthathavepredictivevalue
parameters prior to occurrence of the VAs? for occurrence of VAs.
ii. How to identify abnormal patterns for the
parameters?
iii. How to improve detection accuracy of the ECG 2 Identification and Selection of ECG Parameters
parameters? that have Predictive Value for VA Onset
These problems are selected to be solved since the
There are many ECG parameters that have been reviewed
solutions are beneficial in two aspects. Firstly, they could
before, such as early repolarization, QT/QT dispersion,
speed up the process of reviewing the parameters. It is i
signal-averaged ECG, and HRV [3, 4]. However, these
because the brief information of the parameters will be
reviews still have disadvantages as stated in Sect. 1.
firstly presented in a table before further discussion. The
Moreover,theremighthaveothernewpotentialparameters
solutions simplify relevant information for the ECG
in the recent years. In this case, this paper will select the
parametersandthusresearcherscanhaveaclearideaofthe
source articles for review using approaches as shown in
parameters. Secondly, they provide a way to improve
Fig. 1.
quality of researches. The researches that have unsatisfied
AsillustratedinFig. 1,ECGparameterstobereviewed
prediction result can apply the solutions in the review to
are firstly identified based on existing reviews [3, 4]. This
produce better outcome.
is because some of the parameters in the reviews are still
To address above the problems, this article covers
relevant and reliable in recent studies. Moreover, to
results of works as follows:
increase reliability of this paper, the parameters that are
i. This paper groups the patterns for the increased risk
of the arrhythmias based on behavior changes of
theseECGparametersbeforethearrhythmias.Some
parametersareabsentinnormalconditionofcardiac
patients, and only present before the VAs onset.
There are also parameters that indicate the future
VAs if prolong or decrease until certain ranges. The
behavior changes could be increase, decrease or
presence. The groups of the parameters will be
further discussed in details in Sect. 3.
ii. This paper describes abnormal behaviors of the
parameters according to their normal ranges. As
aforementioned, the parameters, which have value
either higher or lower than certain range, have been
considered as increased risk for the arrhythmic
events.Forinstance,patientswithTpTegreaterthan
100 ms have increased risk of the VT/VF [7–9].
Gender could be a factor that affects the abnormal
range for the parameters, such as HRV and QT
i
[10, 11]. Fig.1 FlowchartofidentifyingexistingECGparameters
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ECGParametersforMalignantVAs 443
lack of related researches in last 10 years are removed. In
addition, the potential parameters that are newly discov-
eredinrecent5 yearswillbeincludedinthereview.Based
on the aforementioned selection, ten parameters from 25
articles will be included for review, as shown in Table 1.
Table 1 presents an overview for the ten parameters.
The first column in the table lists the identified ECG
parameters. The next column related to derivation of the
parametersfromECGsignalcomponents.Thecomponents
of a normal ECG signal are as illustrated in Fig. 2. From
the components of the ECG signals, the parameters are
used in predicting future arrhythmic event of a patient via
morphology based algorithms or measurable values, as
shown in third column. The last column states the predic-
tion of the arrhythmias are successful in patients with
certain diseases.
As it can be seen from the Table, the parameters are
derived from different ECG signal components, such as
QRS complex, which can be used for deriving fQRS, ER
and VLP. In addition, based on methods to identify
Fig.2 ComponentsofnormalECGsignal
parameters, the parameters are categorized into morphol-
ogy based and measurable. QTc/QTvi/QTvi/QTd, HRV, which can be used to derive fQRS, ER and VLP. In
HRT, iCEB, QT dynamicity and TpTe are parameters that addition, based on methods to identify parameters, the
can be easily measured if relevant ECG components are parameters are categorized into morphology based and
detected. In some cases, the parameters might have dif- measurable. QT /QT i/QT i/QT , HRV, HRT, iCEB, QT
c v v d
ferent predictive result in patients with different diseases. dynamicity and TpTe are parameters that can be easily
Therefore, the parameters are tested for various diseases. measured if relevant ECG components are detected. In
The details are further discussed in the following somecases,theparametersmighthavedifferentpredictive
paragraphs. result in patients with different diseases. Therefore, the
As shown in Table 1, the parameters are derived from parameters are tested for various diseases. The details are
different ECG signal components, such as QRS complex, further discussed in the following paragraphs.
Table1 OverviewofECGparameters
ECGparameters Derivedfrom Morphologybased/ PredictionofVAsforpatientswithcertain
measurable diseases
QT/QT i/QT i/QTd QTinterval Measurable RecurrenceofVAs,EF,structuralheartdisease,
c v v
[8,23,24] AMI
fQRS[18–21] QRSduration Morphologybased ARVCM,HOCM,AMI,IDCM
ER[16,22] QRSduration Morphologybased recurrenceofVAs,CAD
VLP[14,15,17] QRSduration-STsegment Morphologybased BS,STEMI
HRV[28,29] RRinterval Measurable AMI
iCEB[30,31] QTintervalandQRS Measurable LQTS,BS
duration
QTdynamicity[26,27] QTintervalandRRinterval Measurable AMI,IDCM
HRT[6,25] RRinterval Measurable AMI
TWA[6,25] Twave/STsegment Morphologybased AMI
TpTe[7–9] Twave Measurable AMI,recurrenceofVAs,Cha-Gas
AMI acute myocardial infarction, BS Brugada syndrome, ARVCM arrhythmogenic right ventricular cardiomyopathy, CAD coronary artery
disease, EF ejection fraction, HOCM hypertrophic obstructive cardiomyopathy, IDCM idiopathic dilated cardiomyopathy, LQTS long QT
syndrome,STEMIacuteST-segmentelevationmyocardialinfarction
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444 S.Mandala,T.C.Di
morphology detection algorithms to include all of their
possible variations [12, 13]. The morphology parameters
occupysmallerpercentagesbecausetheirdetectionhaveto
considervariousmorphologiesoftheparameters.Whereas,
the measurable parameters, such as HRV and QT interval
relatedparametersaremeasuredafterdetectionoftheECG
components. Using the value, abnormality of the parame-
ters can be determined.
TheECGparametersareusefulinpredictingtheVAsfor
patients with certain diseases. As shown in Fig. 5, 35%
studiesassesspredictivevalueoftheparametersinpatients
with acute myocardial infarction (AMI). This is because of
the arrhythmic events are usually occur during the natural
course of AMI [7]. In addition, the parameters [14, 15] are
also prevalent in patients with Brugada syndrome (BS),
whichoccupy13%ofthestudiesintotal.Theparametersare
Fig.3 DerivationoftheECGparameters
less frequent in patients with other diseases, such as
arrhythmogenicrightventricularcardiomyopathy(ARVCM)
Figure 3 shows that the ten parameters in Table 1 are
derived from six different components of the ECG signal. [15],recurrenceoftheVAs[8]andcoronaryarterydisease
(CAD)[16].Thus,therearefewerrelevantstudies.
64% of the parameters can be derived from QRS duration,
RRinterval(RR)andTwave;therestarederivedfromQT
i
intervalandSTsegment.Theformercomponentsareeasier
3 The Ten Potential ECG Parameters
to be acquired since they only involve detection of one
component of the signal, i.e. QRS complex, R wave or T
wave. Thus, most of the parameters, such as fQRS, HRV Section 2identifiestenECGparametersthathavepotential
values to demonstrate underlying ventricular arrhythmias.
andTpTearederivedfromthesecomponents.Whereas,the
These parameters are heart rate corrected QT interval
latter components require detection of both QRS complex
and T wave to derive the parameters, where failure (QTc), QT interval variability (QTvi), QT interval disper-
sion (QT ), fQRS, early repolarization (ER), ventricular
detectionineitheronecomponentcancausethederivation d
late potentials (VLP), HRV, HRT, index of cardio electro
unsuccessful.
Figure 4 illustrates the differences of the parameters in physiological balance (iCEB), QT dynamicity, TWA as
well as TpTe. The parameters have various behavior
pie chart based on methods to identify parameters, which
changes prior to VA onset.
can be categorized into two groups, i.e. morphology based
parameters and measurable parameters. Both occupy 40 Table 2showsthechangesoftheparameterspriortothe
VAs onset can be grouped into three behaviors, i.e. pres-
and 60% of the pie chart respectively. The morphology
ence, increase and decrease. The presence of three ECG
based parameters, such as fQRS and ER are based on
Fig.4 MorphologybasedandmeasurableECGparameters Fig.5 HeartdiseasesandECGparameters
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ECGParametersforMalignantVAs 445
Table2 ECG parameters and their behaviours prior to ventricular researchersshouldidentifythresholdthatrepresentshigher
arrhythmias risk of VAs occurrence. This method may be more suit-
able for group of study subjects that have no gender and
ECGparameter Behavior
agecontrol.Thisisbecauseofgenderandagecouldbetwo
Presence Increase/prolongation Decrease
factors that affect the abnormal ranges.
QT/QT i/QT fQRS 4 In order to know whether the parameters are beneficial
c v d
ER 4 in clinical practice to guide decision making, a cutoff for
VLP 4 predicted probability is needed. An optimal cutoff is
HRV 4 4 defined by decision context. Once the cutoff is selected,
iCEB 4 4 clinical usefulness measures can be defined [32]. The
QTdynamicity 4 measuresusedinreviewedarticlesthatlistedinTable 1are
HRT 4(TO) 4(TS) Pvalue(P),accuracy(Ac),sensitivity(Se),specificity(Sp),
TWA 4 positive predictive value (?P) and area under receiver
TpTe 4 operating characteristic curve (ROC-AUC). According to
the reviewed literatures, P value is the most commonly
used measure. The P value is a probability of obtaining an
observed result, plus more extreme result, assuming the
parameters, namely VLP [14, 15, 17], fQRS [18–21] and truthofthenullhypothesis.StatisticallysignificantPvalue
ER [16, 22] could be associated with an increased inci- (e.g. P\0.05) is not informative about data that are ana-
dence of ventricular arrhythmic events. These three lyzing,i.e.thedataareunlikelywithatruenullhypothesis
parameters appear precede the arrhythmic event. As listed [33]. This provides substantial evidence that the null
inTable 2,itisalsofoundthattheincreasedriskoftheVT/ hypothesis can be rejected. Whereas, ROC-AUC ranged
VFoccurrencecanberepresentedbyeitherprolongationor from 0 to 1. An area of 1 represents a perfect test; an area
reduction of the parameters, based on certain abnormal of 0.5 represents a worthless test [6].
range. The increase or prolongation value of the parame- In addition, Ac is a ratio of total correct assessments to
ters,includingTpTe[7–9],heartratecorrectedQTinterval all assessments, where Ac = (TN ? TP)/
(QT )[8]/QT i[23]/QT [8,24],turbulenceonset(TO)of (TN ? TP ? FN ? FP).Next,Seisrelatedtoanabilityto
c v d
HRT[25],QTdynamicity[26,27],aswellasTWA[6,25] correctly identify patients with VT/VF risk, where
mightindicatehigherriskforthearrhythmias.Table 2also Se = TP/(TP ? FN).Spisrelatedtoanabilitytocorrectly
reveals that the increased risk of the arrhythmias can also identify either normal people or patients with VT/VF risk,
be represented by decrease or reduction in ECG parame- where Sp = TN/(TN ? FP). And, ?P is a probability
ters, such as HRV [28, 29] and turbulence slope (TS) of where a patient with positive test is actuallyat risk of VT/
HRT [6, 25]. The iCEB [30, 31] is the most potential VF, where ?P = TP/(TP ? FP). These four measures
predictor for predicting an increased susceptibility to tor- represent a perfect test if 100% is obtained. Using the
sades de pointes (TdP) or non-TdP VT/VF based on its aforementioned clinical usefulness measures, the ten
increase or decrease value. potentialECGparametersarediscussedtogetherwiththeir
The increase or decrease of the parameters can be behavior changes preceding VAs onset in the following
identified according to certain ranges. In reviewed litera- subsections.
tures, researchers utilized two methods to define abnormal
ranges. Firstly, the researchers match the parameters with 3.1 Ventricular Late Potentials (VLP)
predefined threshold values that indicate the abnormal
ranges. The parameters that have abnormal prolongation TheVLPisahigh-frequencyandvery-low-intensitysignal
prior to the arrhythmias are iCEB, QT dynamicity, QT / that is localized at the end of QRS duration, and on the
c
QT i/QT , TO of HRT, TWA and TpTe. Whereas, the beginning part of ST segment [34], as shown in Fig. 6.
v d
parameters that have lower values than normal heart con- The presence of the VLPs can be identified using pre-
dition are HRV and TS of HRT. The parameters, such as definedthresholds.Accordingtostudies[17,35],theVLPs
fQRS, VLP and ER, in which their presence indicate the existifthreeconditionsofafilteredQRSdurationaremet.
increased risk of VAs can also be identified using prede- The first condition is the filtered QRS duration is at least
fined thresholds. The details of the parameters are dis- 114 ms. And, the filtered QRS duration that has low
cussed further in the following subsections. Secondly, the amplitude signal (\40 lV) in the terminal portion of the
researcherscomparetheparameters intwogroups,namely filtered QRS duration of at least 38 ms. The last condition
healthy people and patients prone to the arrhythmias [28]. is a root mean square of at most 20 lV in the terminal
Using extracted values from these two groups, the portion of the filtered QRS duration of 40 ms.
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446 S.Mandala,T.C.Di
an observed result or more extreme result is obtained in
95.5% of experiments [21].
3.3 Early Repolarisation (ER)
SimilartobothVLPandfQRS,theERcanalsobedetected
at QRS duration. ER is a notching or slurring morphology
of the terminal QRS in at least two contiguous inferior or
lateral leads [16, 38, 39], as depicted in Fig. 8. ER can be
benignormalignant.Ingeneral,malignantERisassociated
with older age, a significantly longer QRS duration and
increased sign of VAs [40]. A prominent J wave is a
noticeable finding just before the VA onset [22, 41]. In
addition, a horizontal or descending ST segment is also
often emphasized in reports of malignant ER [40, 42].
The notched ER and slurred ER patterns can be deter-
Fig.6 MorphologiesofVLPSource[34] mined using an algorithm as shown in Kentta et al. study
[43].ThealgorithmanalyzesECGleadsindependentlyand
The VLP that is detected from signal-averaged electro-
classifies each lead as notched, discrete, slurred, or nega-
cardiogram (SAECG) system could be a useful VT/VF
tive. In addition to these four categories, a fifth category,
predictor for BS and STEMI patients. The VLP has been
indeterminateisusedifthemorphologyofaleadcannotbe
obtainedfrom24 hECGmonitoringproducedaPvalueof
definitely classified by the algorithm.
0.003 in a recent BS study [15]. This indicates that
TheERisassociatedwithincreasedriskoffatalVT/VF
assumingthe VLP has nopredictivevaluefor BS patients,
in patients with CAD [16] and VF history [22]. Based on
an observed result or more extreme result is obtained in
Patel et al. study [16], ER in 12-lead ECG has P = 0.005
0.3%ofexperiments.Moreover,anotherstudy[17]showed
andP = 0.03ininferiorleads(leadsII,III,andaVF).The
that assuming the VLP has no predictive value for STEMI
ER is more common in inferior leads compared to other
patients, an observed result or more extreme result is
leads.ThePvalueoftheERforrecurrentVFis0.008[22].
achieved in less than 5% of experiment.
Assumingthe ER has no predictive value for recurrent VF
patients, an observed result or more extreme result is
3.2 Fragmented QRS (fQRS)
achieved in 0.8% of experiment.
ThefQRSisadditionalspikeswithinQRSdurationwithout
3.4 T peak-T end (TpTe)
bundle branch block [36], which can possess several
morphologies, such as additional R wave, notched R or
TheTpTeisdefinedastimeintervalbetweenTwavepeak
notched S wave [5, 37], as illustrated in Fig. 7.
amplitudeandtheendofTwave[44.TpTecanbeauseful
In a recent study [12], researchers proposed an auto-
predictorforpatientswithdiseases,suchasChagasdisease
mated algorithm based on several thresholds for detection
[9], Brugada syndrome [44], and myocardial infarction
the various morphologies of fQRS. The algorithm has two
[7, 45].
importantmodulesinwhichthedetailsarediscussedinthe
The definition of abnormal prolongation for TpTe is
relevant literature, namely fragmentation detection and
value larger than 100 ms [7–9]. The prolonged TpTe in
morphology identification. The fragmentation detection
12-leads ECG and implantable cardioverter defibrillators
module uses several rules for identifying extrema and
(ICD) indicates VT/VFriskinpatients withAMI[7],VAs
notches based on detailed discrete wavelet transform
history [8] and Chagas disease [9]. These three patient
(DWT) coefficients of QRS duration. On the other hand,
groups have P value of less than 0.01, 0.006 and 0.07.
the morphology identification module encompasses rec-
ognization of six fundamental morphologies of fQRS and
other variations of RSR’ patterns. 3.5 QT Interval Related Parameters (QT)
The fQRS is a significant predictor for patients with
ARVCM [18], HOCM [19] and IDCM [21]. The fQRS on TheQT ismeasuredfromthestartofQwavetotheendof
i
12-lead ECG produced P\0.001 and P\0.05 in Can- T wave [46]. QT is QT corrected for heart rate [8, 46].
c i
polat et al. [18] and Femenia et al. [19] studies respec- QT isdifferencebetweenmaximumandminimumofQT
d i
tively.IfthefQRShaspredictivevalueforIDCMpatients, [24]. And, QT i is a ratio comparing repolarization
v
123
ECGParametersforMalignantVAs 447
Fig.7 MorphologiesoffragmentedQRSSource[36]
Fig.8 Morphologiesofearlyrepolarization
variability to HRV [47]. Equations for the QT related where RR is measured from one R wave to the next R
i i
parameters are as shown in Eqs. 1, 2, and 3. wave before the QT
i
QT ¼QT (cid:3)QT ð2Þ
d max min
p
QTcðBazettformulaÞ¼QTix ð1=RRÞ ð1Þ QTvi¼logð(cid:2)varianceQTi=meanQT2(cid:3)=(cid:2)varianceRR=meanRR2(cid:3)Þ
ð3Þ
123
448 S.Mandala,T.C.Di
The abnormal ranges for QT , QT and QT i are values
c d v
greaterthan460 ms,65 msand-0.47respectively[8,48].
According to Cox [10], the normal QT usually ranged
i
from 0.36 to 0.44 s. From these ranges, adult males have
shorter range than adult females, and peoplefrom age 1to
15areinbetweenthem,asshowninTable 3.Accordingto
the table, QT with value greater than 0.47 s is considered
i
as dangerous for any gender or age.
Measuring of the QT related parameters can detect
patientswhoareatincreasedriskofdevelopingVT/VFand
Fig.9 MeasurementofTWA
with diseases such as structural heart disease [23], AMI
[24]andVAshistory[8].IftheQT ionICDhaspredictive
v
valueforpatientswithstructuralheartdisease,anobserved
result or more extreme is obtained in more than 96% of
experiments [21]. Assuming the QT has no predictive
i ðRR þRR Þ(cid:3)ðRR þRR Þ
value for patients with no recurrence of VAs and AMI, an TO¼ 1 2 (cid:3)2 (cid:3)1 (cid:4)100% ð5Þ
RR þRR
observed result or more extreme result is achieved in less (cid:3)2 (cid:3)1
than 1.1 and 0.1% of experiments respectively [8, 24]. where RR and RR are two intervals immediately
-2 -1
preceding ventricular pre- mature complexes (VPC); and
3.6 T Wave Alternans (TWA) and Heart Rate RR1 and RR2 are two RR intervals immediately after
Turbulence (HRT) compensatory pause.
The another parameter derived from HRT is TS, which
The TWA is arepeating ABAB pattern inthe morphology represents the deceleration of heart rate. The TS can be
andamplitudeofTwaveorSTsegment[49],asillustrated measuredbasedonthemaximumpositiveregressionslope
in Fig. 9. It reflects a continuum of cardiac electrical assessed over any five consecutive sinus rhythm RR
instability [50]. interval within the first 15 sinus rhythm RR intervals after
The TWA can be either an independent predictor [51] ventricular premature contraction [25]. Therefore, the TO
using Eq. 4 or a combined predictor with HRT [6, 25]. can be defined as measure of relative shortening of RR
However, TWA might not be a useful predictor for exer- after pause of ventricular premature beat.Whereas, the TS
cising individuals since it can be occurred in normal indi- characterizes subsequent lengthening of RR [6].
viduals at heart rates more than 120 beats/min [52]. Both HRT and TWA have different thresholds to iden-
tify abnormal behavior. The abnormal TO of HRT in
P (cid:3)l patients have value 0% and above. On the other hand, the
Kscore¼ 0:5 ð4Þ
r abnormal TS of HRT is value less than 2.5 ms per RRi
[25,54].And,theabnormalprolongationofTWAisvalue
wherelandraremeanandstandarddeviationofspectral
more than 46 lV [6, 25].
noise, P is alternans power at 0.5 cycles/beats and
0.5 The combined TWA and HRT on 12-leads ECG and
(P - l) is alternans voltage. The TWA is considered as
0.5 implantable cardioverter defibrillators (ICD) indicates VT/
significant if K score is more than three [53].
VF risk in patients with AMI [6, 25]. According to Arisha
TheHRTisashort-termfluctuationinsinuscyclelength
et al. study [6], a combined TO of HRT and TWA on
thatfollowsspontaneousventricularprematurecomplex,in
channel 1 has strong predictive performance for VT/VF,
whichconsistsofbriefheartrateacceleration,followedby
where ROC- AUC = 0.8, P = 0.03 Se = 80% and
moregradualheartratedeceleration[54].Theacceleration
Sp = 79%. In addition, both abnormal TWA and HRT
is quantified by TO. The TO can be represented as Eq. 5.
have achieved P value 0.002 [25].
Table3 NormalQTirangesSource[10] 3.7 Heart Rate Variability (HRV)
Age1–15 Adultman Adultwoman
The HRV has both time domain and frequency domain
Normal \0.44s \0.43s \0.45s variables that have predictive power for the arrhythmic
Borderline 0.44–0.46s 0.43–0.45s 0.45–0.47s events.Examplesofthetimedomainvariablesarestandard
Prolonged [0.46s [0.45s [0.47s deviation of all RR/normal-to-normal (NN) intervals
(SDNN) and mean of RR/NN interval (RRm) [28], which
ssecond
123
ECGParametersforMalignantVAs 449
can be expressed as Eqs. 6 and 7; examples of the fre- 3.8 Index of Cardiac Electrophysiological Balance
quency domain features are low frequency (LF) and high (iCEB)
frequency (HF) components [28, 55].
X The iCEB is a hypothesized that equivalent to cardiac
RR ¼1=n RR ð6Þ
mean i wavelength, which is a product of effective refractory
SDNN ¼qffi1ffiffi=ffiffinffiffiffiffiXffiffiffiffiffiffiðffiffiRffiffiffiRffiffiffiffiffi(cid:3)ffiffiffiffiffiRffiffiffiRffiffiffiffiffiffiffiffiffiffiffiÞffi2ffiffi ð7Þ period and conduction velocity (ERP 9 CV). It reflects
i mean balance and imbalance of depolarization (QRS duration)
and repolarization (QTi) of the cardiac electrophysiology,
The abnormal HRV can be recognized by comparing
asshowninFig. 10[30,31].TheiCEBcanberepresented
bothnormalrangeofHRVinhealthypeopleandabnormal
as in Eq. (8). Lu HR et al. [31] enhanced their previous
range in patients prone to the arrhythmias. In average,
study [30], which investigated predictive value of iCEB in
normal range of HRV for females is low than males [56].
both TdP and nonTdP mediated VT/VF. Based on [31]
Statistics for the normal ranges of HRV parameters are as
study, females have higher value of iCEB compared to
illustratedinTable 4.Comparisonofbothtimedomainand
males, which are mean of 4.583 and 3.989 respectively.
frequency domain parameters amongst males and females
However, data in the study demonstrated that age has no
showed attenuated HRV in females. Among the HRV
major influence on the iCEB.
parameters, SDNN and LF are found significantly
i
decreased in females, i.e. P\0.05. iCEB¼QT=QRS ð8Þ
Inthisreview,theHRVistheonlyonepredictorthatis
The iCEB increased prior to TdP VT/VF (LQTS) and
used in short term VT/VF prediction. Ebrahimzadeh et al.
decreased before non-TdP VT/VF (BS). Assuming the
[28]showstime–frequencyandnon-linearfeaturesofHRV
iCEB has no predictive value for both LQTS and BS, an
on 24 h ECG can predict the VT/VF that cause sudden
observed result or more extreme result is achieved in less
cardiac death. The prediction time is four minutes prior to
than 0.01% of experiments [31].
its occurrence, with accuracy more than 83%. Rozen et al.
study[29]alsoreveals thatHRVispredictorforimminent
3.9 QT Dynamicity
VT with 50% Se, 91.6% Sp and 84.5% ?P.
TheQTdynamicityrepresentsrelationshipofQT andRR
i i
[57],whichmeasuredbyslopeoflinearregressionofQT/
i
RR, as expressed in Eq. (9). The QT/RR slopes are dif-
i
ferentinfemalesandmales:femaleshavesteeperorhigher
value slopes than males [26, 57].
Table4 Comparison ofHRV parameters between male and female
QTdynamicity¼QT=RR ð9Þ
subjectsSource[56] i i
HRVparameters Groupofindividuals Pvalue The QT dynamicity is a useful predictor for patients
with IDCM [26] and AMI [27]. The QT dynamicity on
Males Females
Timedomain(ms)
SDNN 140±36 122±33 0.09
SDANN 123±34 111±34 0.23
SDNN 64±19 52±14 0.03
i
rMSSD 40±14 40±22 0.9
pNN50 14±10 12±7 0.43
Frequencydomain(ms2)
Totalpower 4041±3150 2750±1493 0.07
VLF 2912±2675 1843±928 0.06
LF 788±397 556±346 0.04
HF 318±251 312±277 0.94
SDNNstandarddeviationofRRintervals,SDANNstandarddeviation
of average NN intervals, SDNN SDNN index, a measure of vari-
i
ability due to cycles shorter than 5min, rMSSD square root of the
meansquareddifferencesofsuccessiveNNintervals,pNN50number
ofintervaldifferencesofsuccessiveNNintervalsgreaterthan50ms,
NN50/totalnumberofNNintervals,VLFverylowfrequency,LFlow
frequency,HFhighfrequency Fig.10 MeasurementofiCEBSource[31]
123
450 S.Mandala,T.C.Di
12-lead ECG is significantly associated with the VT/VF Therearealsosomeadditionalrecommendedsettingsin
event (P\0.001). Assuming the QT dynamicity has no the reviewed literatures for the parameters. For instance,
predictive value for IDCM patients, an observed result or suggested frequency band for LF and HF in HRV are
more extreme result is obtained in less than 0.1% of 0.04–0.15and0.15–0.4 Hzrespectively[28,55].Whereas,
experiments [26]. a higher cutoff frequency of low-pass filter (150 Hz) is
needed in order to detect fQRS, else additional spikes
within a QRS complex will be masked [36].
4 Methods to Improve Detection
of the Parameters
5 Discussion
Parameters detection is done after preprocessing, i.e.
As showed in Sect. 2, there are ten ECG parameters have
denoise and restructure of ECG signals. However, false
been reviewed, namely QT /QT i/QT i/QT , fQRS, ER,
detection of the parameters always happens. This may due c v v d
VLP,HRV,HRT,TWAaswellasTpTe.Theseparameters
to misclassification of wide QRS duration as prolonged
are popular in recent studies and thus investigation
QT, irregular heart rhythms, as well as slow transition of
i regarding to the parameters is necessary.
thesignalaroundTwaveend[10,13].Inordertoincrease
The aforementioned parameters have achieved signifi-
correct detection rate of the parameters identified in
cant prediction results in most of the studies; however,
Sect. 3, three methods are suggested in this paper.
some literatures reported that predictive value of the
The first method is averaging, i.e. mean for datasets of
parameters in patients with certain heart diseases are
parameters. This method is useful to resist fluctuation
insignificant. From Table 1, it is showed that fQRS have
between the data of each parameter. In this case, Cox [10]
received more attention than other six parameters. The
recommended to measure the QT by averaging five or six
i fQRS isapossiblenewindextoidentify high-risk patients
QT. Similar for TS of HRT, calculation of TS is based on
i inarrhythmicevents[18],itsprognosticroleislessknown
maximum positive regression slope assessed over any five
and therefore many studies are done to confirm its pre-
consecutive sinus rhythm RR interval within the first 15
dictive value. In the study [59], fQRS in patients with
sinus rhythm RR intervals after ventricular premature
Chagas’s cardiomyopathy are failed to predict the
contraction [25].
arrhythmic events, although there are many successful
The second method is handling of outliers. Presence of
evidences in patients with AMI [20], HOCM [19], as well
outliers,evenaverylittleamountcanproduceasignificant
as ARVCM [18]. Similar to fQRS, the VLP is a poor
changeintheparameters.Afteridentifiedtheoutliers,either
predictor in patients with NSTEMI and ARVCM [15, 17].
tossing (removing the outliers) or interpolation (replacing
Furthermore, most of the reviewed literatures may only
theoutliers)canbeperformed[58].Thedefinitionofoutliers
reflect the occurrence of VT/VF after a few months or
may vary among the parameters. For example, there are
years (long-term) [6–9], but not minutes or hours (short-
somerecommendationforHRTdetectionduringfilteringof
term). Only HRV has shown short-term predictive value
RRisequences.Thefilteringshouldincludeeithersubstan-
forthearrhythmicevent[28,29].However,thereisanother
tial beat to beat RR intervals difference that are more than
study found no significant association between short-term
200 ms or substantial difference from an average of five
predictionandparameterssuchasHRVandQT [60].The
precedingsinusRRintervalsthataremorethan20%.And, vi
conflicting result of HRV as short-term predictor in both
thefilteringisrecommendedtoexcludeRRintervalsthatare
studies [28, 60] may due to small sample size and HRV
notinrangeof300–2000 ms(outliers)[54].
parameters choose in the VAs prediction.
The third method is application of morphology based
Section 3hasshowntherearethreemainpatternsofthe
algorithms,inwhichasdiscussedinSect. 3.Thismethodis
parameters that are associated with future VAs, i.e. pres-
faster than traditional visual assessment (determination of
ence, prolong and decrease. Among these patterns, the
theparametersbyexperiencedECGsignalreaders).And,it
presence of the fQRS, ER and VLP can be used for indi-
is especially useful for detection of multiple variations of
cating the arrhythmic events. However, the prolongation
ECG components such as fQRS and ER. The detailed
and decrease of the parameters need a certain range to
algorithms are as discussed in [12, 13, 43]. The morphol-
determine whether the arrhythmia is coming. The ranges
ogy detection algorithm for fQRS is based on number of
that represent the abnormality of the parameters were also
identified extrema and notches, point of occurrence of
discussed in Sect. 3.
discontinuities, as well as morphology of fragmentation
The ranges can only be fit after the parameters have
[12].Whereas,thenotchedERandslurredERpatternsare
been detected and measured. The detection can be missed
determined using algorithm as in Kentta et al. study [43].
and the measurement can be inaccurate if lack of
123