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Medical Engineering & Physics 35 (2013) 16– 22

Contents lists available at SciVerse ScienceDirect

Medical Engineering & Physics

j o ur nal homep age : www.elsev ier .com/ locate /medengphy

ovel set of vectorcardiographic parameters for the identification of ischemicatients

aúl Correaa,∗, Pedro D. Arinib,c, Max E. Valentinuzzi c, Eric Laciara

Gabinete de Tecnología Médica, Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, ArgentinaInstituto Argentino de Matemática (IAM) “Alberto P. Calderón”, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, ArgentinaInstituto de Ingeniería Biomédica (IIBM), Facultad de Ingeniería (FI), Universidad de Buenos Aires (UBA), Buenos Aires, Argentina

r t i c l e i n f o

rticle history:eceived 6 August 2011eceived in revised form 24 January 2012ccepted 12 March 2012

eywords:RS-loopyocardial ischemia

CG

a b s t r a c t

New signal processing techniques have enabled the use of the vectorcardiogram (VCG) for the detec-tion of cardiac ischemia. Thus, we studied this signal during ventricular depolarization in 80 ischemicpatients, before undergoing angioplasty, and 52 healthy subjects with the objective of evaluating the vec-torcardiographic difference between both groups so leading to their subsequent classification. For thatmatter, seven QRS-loop parameters were analyzed, i.e.: (a) Maximum Vector Magnitude; (b) Volume; (c)Planar Area; (d) Maximum Distance between Centroid and Loop; (e) Angle between XY and OptimumPlane; (f) Perimeter and, (g) Area-Perimeter Ratio. For comparison, the conventional ST-Vector Magnitude(STVM) was also calculated. Results indicate that several vectorcardiographic parameters show significant

T-vector magnitude differences between healthy and ischemic subjects. The identification of ischemic patients via discrim-inant analysis using STVM produced 73.2% Sensitivity (Sens) and 73.9% Specificity (Spec). In our study,the QRS-loop parameter with the best global performance was Volume, which achieved Sens = 64.5% andSpec = 74.6%. However, when all QRS-loop parameters and STVM were combined, we obtained Sens = 88.5%and Spec = 92.1%. In conclusion, QRS loop parameters can be accepted as a complement to conventionalSTVM analysis in the identification of ischemic patients.

© 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

. Introduction

Cardiac ischemia is characterized by an imbalance betweenyocardial oxygen supply and demand. It is frequently associatedith coronary atherosclerosis, which hinders the normal coro-ary blood flow. It is well-known that insufficient myocardial cell

rrigation reflects in the electrocardiogram (ECG) as ST-segmenteviation [1] and T-wave alterations [2,3]. Besides, Pueyo et al.emonstrated that the upward and downward slopes of the QRSomplex decreased during artery occlusion [4]. Similarly, Toledond Wagner proposed an analysis of high-frequency QRS compo-ents to identify cardiac ischemia [5]. In all cases, the amplitude and

emporal changes of the QRS complex during an ischemic episodere spatially related to the ischemic area. Physiologically, theyight be traced back to conduction disturbances in the ischemic

∗ Corresponding author at: Gabinete de Tecnología Médica, Facultad de Inge-iería, Universidad Nacional de San Juan, Av. Libertador General San Martín 1109O), J5400ARL San Juan, Argentina. Tel.: +54 264 4211700x313.

E-mail addresses: rcorrea@gateme.unsj.edu.ar, rraul oscar@yahoo.com.arR. Correa), pedro.arini@conicet.gov.ar (P.D. Arini), maxvalentinuzzi@arnet.com.arM.E. Valentinuzzi), laciar@gateme.unsj.edu.ar (E. Laciar).

350-4533/$ – see front matter © 2012 IPEM. Published by Elsevier Ltd. All rights reserveoi:10.1016/j.medengphy.2012.03.005

segment. In other words, not only the waveform and segmentcharacteristics of cardiac electrical activity are modified during anepisode of hypoperfusion but there is need, too, of new parametersto improve ischemic diagnosis.

Besides, the Vectorcardiogram (VCG) has been proposed to eval-uate cardiac changes during ischemia or infarction. In an earlywork, Wolff used the spatial VCG to study initial, early, and ter-minal “electrical forces” related to ventricular depolarization andconcluded that those “forces” can be more accurately describedby VCG than ECG [6]. Sederholm et al. demonstrated that spatialST-Vector Magnitude (STVM) and QRS-Vector Difference (QRSVD)behave as complementary measures in the quantification of evolv-ing myocardial injury after acute coronary occlusion and in thedetermination of sequels to therapeutic interventions [7]. Erikssonused VCG monitoring to identify myocardial reperfusion at an earlystage and to give valuable prognostic information in patients withunstable angina and acute myocardial infarction [8]. Kawahito et al.concluded that monitoring STVM along with its QRSVD may be usefulfor identifying myocardial ischemia during carotid endarterectomy

[9]. Similarly, Dellborg et al. added that monitoring of QRS- andST-Vector combinations may be a highly sensitive method fordetecting myocardial ischemia [10]. Recently, Pérez Riera et al.exposed the advantages of the computerized VCG compared to

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neering & Physics 35 (2013) 16– 22 17

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he ECG, particularly by showing better specificity, sensitivity andccuracy as compared with conventional ECG for the diagnosis ofeveral cardiac pathologies [11]. Hence, the need of new VCG ana-ytical methods to extract hidden diagnostic information appearsppropriate and timely.

As a consequence, the aim of this study was to differenti-te a group of ischemic subjects from a population of healthynes by means of the vectorcardiographic analysis of ventricu-ar depolarization. For that matter, seven QRS-loop parameters

ere computed and, afterwards, they were contrasted againsthe conventional STVM within a patient classification scheme. Ourypothesis was that the morphological QRS-loop changes dueo myocardial ischemia should contribute to, or even perhapsmprove, the identification of such patients. A preliminary studyroduced promising evidence [12].

. Materials

Raw clinical records were extracted from the PTB diagnostic ECGnd STAFFIII databases.

The first one contained the ECG records of 52 healthy sub-ects (39 men, age 42 ± 14 yrs, and 13 women, age 48 ± 19 yrs). TheCGs in this collection were obtained by the National Metrologynstitute of Germany. Each record included 15 simultaneously mea-ured signals: the conventional 12 leads (I, II, III, aVR, aVL, aVF,1–V6) together with the 3 Frank lead ECGs (X, Y, Z). Each sig-al was digitized at 1000 Hz, with 16 bits of amplitude resolution13,14].

The second database consisted of 80 ischemic patients (50en, age 60 ± 12 yrs, and 30 women, age 62 ± 11 yrs), from the

harleston Area Medical Center in West Virginia, before angioplastySTAFFIII study). Nine standard ECG leads (V1–V6, I, II, III) were dig-tized at a sampling rate of 1000 Hz and an amplitude resolutionf 0.6 �V. A more extensive description of the STAFF III databases found in [15,16]. Synthesized orthogonal X, Y and Z leads werebtained by the Kors transform [17]. A recent study has demon-trated that Kors synthesis matrix provides a better estimation ofrank leads than the Inverse Dower transform in ischemic patients18].

. Methods

Fig. 1 illustrates a block diagram of the different stages of thenalysis. These stages will be explained in the following subsec-ions.

.1. Preprocessing

Firstly, all ECG records were preprocessed with a band-pass fil-er (Butterworth, 4th order, 0.2–100 Hz, bidirectional) to reduceow and high frequency noise and a notch filter (Butterworth, 2thrder, 50/60 Hz, bidirectional) to minimize the power-line interfer-nce. A cubic spline interpolation filter was used to attenuate ECGaseline drifts and respiratory artifacts [19]. Thereafter, the QRSomplexes and their endpoints were detected in each ECG recordsing a modified version of that proposed by Pan and Tompkins20]. Excessively noisy beats (with a RMS noise level >40 �V, mea-ured within a 40 ms window located at 2/3 of the RR interval) were

xcluded. In addition, ectopic beats were also eliminated by com-aring incoming signals against a previously established templateith the use of a cross-correlation technique (we used a correlation

95% as acceptance level). In this study, a visually low-noise normaleat extracted from the ECG record is selected as a template beat,s proposed in [21].

Fig. 1. General diagram of the proposed analysis technique.

3.2. VCG estimation

The VCG was obtained by drawing simultaneously in a 3-D plotthe instantaneous amplitudes of X, Y and Z orthogonal leads for eachsample in the temporal interval corresponding to the QRS complex,that is, from the starting point of the Q-wave (or R, if there was noQ) to the final point of the S-wave (or R, if there was no S).

3.3. QRS-loop alignment

The alignment of all QRS-loops in each ECG record is requiredin order to compensate for changes induced by extracardiac fac-tors, such as respiratory movements [22]. This alignment can beresolved by obtaining the Rotation (R) and Translation (T) matricesthat allow the beat-to-beat QRS-loop alignment against a patternor template QRS-loop, the latter obtainable from the averagedQRS-complex. Herein, we calculated the template QRS-loop as theaveraged of each normal sinus QRS-loop, i.e. averaging the tempo-rally aligned QRS complexes in X, Y and Z leads, detected at the firstminute of each ECG record using the standard methodology [23].

Such matrices were computed from an adapted version of thealgorithm [24], which can be summarized in the following 4 steps:

(1) The coordinates X(i), Y(i) and Z(i) of the QRS pattern and QRSindividual loop are grouped into {pi} and {qi} sets, respectively,with i = 1,. . ., N and N standing for the total number of samplesof the QRS-complex.

(2) Both sets {pi} and {qi} are arranged in two 3 × N matrices,designed as P and Q, respectively; thereafter, the covariancematrix H is determined as QPT.

(3) The Singular Value Decomposition technique is applied to theH-matrix, wherefrom three matrices result, namely, U, � andV, where H = U�VT; hence, the R-matrix can be computed asR = V UT.

18 R. Correa et al. / Medical Engineering & Physics 35 (2013) 16– 22

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Individual QRS loopsIndividual QRS loop Centroids

Template QRS loop Template QRS loop Centroid

Fig. 2. Example of spatial alignment of individual QRS loops for 4 individuals beats extracted from control records of an ischemic patient (Record # 25, STAFFIII Database).(

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4) The T-matrix is calculated as the difference between the coor-dinates of the QRS-loop centroid (pc) and the centroid of theindividual QRS-loop (qc), that is T = pc − R × qc.

Fig. 2 illustrates the QRS-loops alignment. On the left side (a), ithows 4 non-aligned QRS-loops with their corresponding centroidsnd the template QRS-loop, whereas on the right (b), it displayshe same 4 QRS-loops after alignment. The small alignment errorsbserved are produced by normal beat-to-beat morphological dif-erences.

.4. Parameters’ computation

Seven vectorcardiographic parameters were computed from theRS-loop for each detected beat. For comparison, the conventionalTVM was also computed.

QRS-loop Maximum Vector Magnitude (QRSmaxVM ): The vector mod-

lus for each QRS coordinate (X, Y, Z) was initially calculated,hereafter, the maximum value was obtained [25]. It describes the

aximum magnitude of the Depolarization Vector (Fig. 3).QRS-loop Volume (QRSV): It estimates the 3-D depolarization

oop curve volume and allows quantifying the loop flatness. Tochieve a more accurate estimation, it has been found that the pointet that produces the minimum convex volume and contains alloints within is obtained by means of the Convex Hull algorithm26].

QRS-loop Planar Area (QRSPA): It is the estimated area of the loopbtained by projecting the QRS-loop on the best adjusted planeomputed by least mean squares (denoted as QRS-proj and Opti-um Plane, respectively). It is thought to reflect hemodynamic

bnormalities in cardiac lesions [27]. The QRS-loop area computednto optimum plane provides a more exact estimation of the real-D QRS-loop area than those calculated on the conventional XY,

Z or ZY planes (Fig. 3).

Maximum Distance between the QRS-centroid and the QRS-loopQRSmax

DCL ): The centroid loop is initially estimated and, there-fter, the Euclidean distance from this centroid to each point of

the loop is determined searching for its maximum. This param-eter measures a relative distance that is independent on theposition of the loop in 3-D, unlike QRSmax

VM , which measures anabsolute distance with respect to the reference system origin(Fig. 3).

Angle between the XY-plane and the Optimum Plane (QRSAngleXYOP): It

evaluates the deviation between the Optimum Plane and the refer-ence frontal plane (XY) in the 3-D space. Since the loops are spatiallyaligned, the angle variations between both planes are only due tomorphological changes of the QRS-loop.

Area/Perimeter Ratio (QRSRatioAP ): This ratio is evaluated over the

QRS-loop projected over the Optimum Plane. It served in the anal-ysis of loop morphological changes.

Perimeter (QRSP): It is the Perimeter computed over the QRS-loop projected over the Optimum Plane. It measures the loop totallength and can detect loop contour changes.

ST-Vector Magnitude (STVM): It is a widely used parameter whenmonitoring cardiac ischemia [8,9] and is defined as the magnitudeof the vector composed of the (X, Y and Z) ST-segment deviationsfrom the isoelectric level (STVM =

√ST2

X + ST2Y + ST2

Z ), measuredas the amplitude of the ECG signal at the J-point. This point wasestimated for each beat as Jk = 40 + 1/3 (RRk)1/2 (in milliseconds),where k denotes the k-th beat and RRk is the RR interval betweenthe current beat and the following one [28].

Fig. 3a shows some parameters computed in 3-D plot of an indi-vidual QRS-complex extracted from an ECG. Moreover, in Fig. 3bthe QRS loop projections on orthogonal conventional planes andthe corresponding (X, Y, Z) ECG leads are shown. The J-point isrepresented by a red dot.

3.5. Statistical analysis and classification method

All parameters were computed for each detected sinus beat in

every ECG record. First, we analyzed the normality of these val-ues using D’Agostino-Pearson test with the aim of quantifying thediscrepancy between the parameters distribution and the Gaus-sian distribution. Afterwards, comparisons between groups were

R. Correa et al. / Medical Engineering & Physics 35 (2013) 16– 22 19

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ig. 3. (a) Some VCG characteristic parameters computed in the 3-D QRS loop for aTAFFIII Database). (b) QRS loop projections on XY, XZ, YZ planes (frontal, transverespective J-points (denoted with a red dot). (For interpretation of the references to

ade using the non-parametric Mann–Whitney test because thenderlying variables distribution was non Gaussian.

Thereafter, the mean value of each parameter across the entireecord was calculated. These values were used as inputs to a clas-ifier based on Linear Discriminant Analysis (LDA) to distinguish (oreparating out) ischemic patients from healthy subjects.

Basically, the LDA classifier is a linear combination of variables,s follows,

= �0 + �1X1 + �2X2 + · · · + �pXp

here y is the output value of the discriminant function; �n (with = 1,. . ., p) are the coefficients of the discriminate function; Xn arehe discriminate variables (QRS loop parameters and/or STVM) and

is the number of variables in the analysis [29].The resulting discriminant function can be used to assign each

CG record to a particular group or class (in this case, ischemicatient or healthy subject), based on its values of discriminate vari-bles. The model coefficients are estimated with a subset of ECGecords for which the group is known. This subset of observations

s sometimes referred to as the training subset (we used the 70% ofCG records of both populations).

In order to validate the model, this discriminant function wassed to predict the group of another different subset (referred to as

vidual beat extracted from the control record of an ischemic patient (Record # 51,d left sagittal, respectively) and temporal representation of X, Y, Z leads with their

in this figure legend, the reader is referred to the web version of the article.)

validation subset) of the ECG records (we used the remaining 30%of the records).

To evaluate the performance of the LDA classifier, we computedthe Receiver Operating Characteristic (ROC) curve. It plots theSensitivity (Sens) against the 1-Specificity (Spec) values for the dif-ferent possible cut-off points (we vary the cut-off values between−5 and 5 in 0.01 steps) of the discriminant function.

Then, the optimal cut-off point in the ROC curve was computedas the point nearest the top left-hand corner. This selection maxi-mizes the Sens and Spec sum, when it is assumed that the ‘cost’ ofa false negative result is the same as that of a false positive result[30]. Finally, the global performance of the classifier was evaluatedwith the Area Under the ROC Curve (AUC).

4. Results

In this study seven vectorcardiographic parameters were eval-uated on 3-D QRS loops to quantify and assess the morphological

differences between ECG recordings of ischemic patients andhealthy subjects. Fig. 4 illustrates QRS-loops obtained from one reg-ular beat of a healthy subject and an ischemic patient (the samerecord appears in Fig. 3), respectively. It can be seen that both

20 R. Correa et al. / Medical Engineering & Physics 35 (2013) 16– 22

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Fig. 4. QRS loops of a healthy subject (Record # 2, PTB Database) and an ischemic patient (Record # 51, STAFFIII Database).

Table 1Mean and standard deviations of QRS loop parameters and STVM.

QRSmaxVM [mV] QRSV [mV3] QRSPA [mV2] QRSmax

DCL [mV] QRSAngleXYOP [rad] QRSRatio

AP QRSP [mV] STVM [mV]

Healthy subjects 1.55 ± 0.58 0.15 ± 0.16 1.20 ± 0.65 1.03 ± 0.34 1.90 ± 0.21 4.44 ± 1.92 4.84 ± 1.31 0.05 ± 0.03.98 ±

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Ischemic patients 1.23 ± 0.37* 0.06 ± 0.06* 0.79 ± 0.50* 0

* Indicated p-value < 0.01, ischemic patients versus healthy subjects.

RS-loops exhibit important morphologic differences, whichould be quantified by the proposed parameters.

Table 1 shows the mean and standard deviation values com-uted for each index of both populations. The values marked with

in Table 1 indicate the statistical significance (p-value < 0.01).Fig. 5 illustrates the dispersion of the discriminant function

alues for two different training and validation subsets. The

ashed-dot line represents the default cut-off point = 0. It can bebserved that the Sens and Spec values (computed in the validationtage) are different in (a) with respect to (b).

0 20 40 60 80 100 120

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valu

es

# Record

(a)

Healthy Training Subset Healthy Validation Subset

Sens = 87.5% Spec = 87.5%

ig. 5. Dispersion of the discriminant function values for two different training and valiut-off point (by default equal to 0).

0.29 1.86 ± 0.22 5.45 ± 1.64* 3.86 ± 0.96* 0.12 ± 0.10*

Since these outcomes depend on the records chosen for thetraining and validation stages (see Fig. 5), we randomly selected100 different training and validation subsets to obtain more preciseresults. We used these subsets to compute 100 different discrimi-nant functions.

Fig. 6 shows the ROC curve, built with the mean values of theSens and (1-Spec) obtained for the 100 discriminant functions using

all QRS-loop parameters and the STVM index. In the same figurewe marked with a red circle the optimum cut-off point and itscorresponding Sens and Spec values.

Ischemic Training Subset Ischemic Validation Subset

0 20 40 60 80 100 120

−10

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# Record

Sens = 100% Spec = 100%

dation subsets of healthy and ischemic subjects. The dash-dot line represents the

R. Correa et al. / Medical Engineering & Physics 35 (2013) 16– 22 21

Table 2Classification results for QRS loop parameters and STVM.

QRSmaxVM QRSV QRSPA QRSmax

DCL QRSAngleXYOP QRSRatio

AP QRSP STVM QRS loopparameters andSTVM

Sens (%) 56.67 64.46 70.50 38.46 54.58 71.63 69.87 73.25 88.46Spec (%) 68.00 74.56 72.50 60.38 48Cut-off −0.66 −0.72 −0.61 −0.49 −0AUC 0.67 0.76 0.73 0.47 0

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 1

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1

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sibi

lity

1−Specificity

Cut−off point = −0.35Sens = 88.46%Spec = 92.13%

Fig. 6. ROC curve and optimum cut-off point (indicated with the red circle). For theoit

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ptimum cut-off point = −0.35, it is obtained Sens = 88.46% and Spec = 92.13%. (Fornterpretation of the references to color in this figure legend, the reader is referredo the web version of the article.)

Table 2 shows the Sens and Spec mean values, the optimal cut-ff point and the AUC, for different classification schemes usingach individual QRS loop parameter, the STVM index and the com-ination of all QRS loop parameters and STVM.

. Discussion and conclusions

In the last years, numerous papers have proposed differentechniques to detect and classify changes in cardiac electrical activ-ty recorded from the surface ECG in patients with myocardialschemia [1–5]. This cardiopathy is usually diagnosed on the ECGy the measurement of ST deviation at the J-point [31]. However,ope et al. showed that only 410 of 1445 patients with acute car-iac ischemia who presented to the emergency departments of ten.S. hospitals had significant ST deviation [32]. Despite this limi-

ation of the standard electrocardiography, this record remains ashe most important criterion for the clinical evaluation of patientsith chest pain [33].

A recent review demonstrates the superiority of the VCG basedechniques versus those based on the ECG alone; vectorcardiogra-hy provides a better and more rational insight into the electricalhenomena that occur spatially [11]. Several researchers have pro-osed the analysis of the VCG for studying cardiac diseases, such asyocardial ischemia and infarction [6–10].The present study examines the 3-D VCG of ischemic patients

nd healthy subjects to distinguish between them. In order to eval-ate the discrepancy in these VCG’s, seven QRS loops parametersere estimated: QRSmax

VM , QRSV, QRSPA, QRSmaxDCL , QRSAngle

XYOP , QRSP, and

RSRatioAP . For comparison, the conventional STVM index was also

omputed.On the basis of the descriptive analysis of the studied param-

ters (shown in Table 1), it can be observed that five QRS-loop

.38 66.13 69.50 73.94 92.13

.43 −0.50 −0.33 −0.27 −0.35

.51 0.75 0.70 0.79 0.90

parameters and STVM index have significant differences (p-value < 0.01) between two populations. It indicates that theproposed vectorcardiographic technique can be used to detectmyocardial ischemia.

In addition, the results of the discriminant analysis (shown inTable 2) indicate that QRSV is the individual QRS-loop param-eter with the best global performance (AUC = 0.76), obtainingSens = 64.5% and Spec = 73.6%. Moreover, the identification ofischemic patients is greatly enhanced when all the QRS-loopparameters in combination with the STVM index are used in theclassification, achieving Sens = 88.5% and Spec = 92.1%. Meanwhile,when we used only the conventional STVM index, we obtainedSens = 73.3% and Spec = 73.9%, which are considerably lower thanthose reported above. The poor performance achieved with the ST-level analysis agrees with the study previously reported by Fayneet al. [34].

Additionally, the AUC value (shown in Table 2) for the classifi-cation using all the QRS-loop parameters in combination with theSTVM index is AUC = 0.90, which indicates high effectiveness of theproposed classification technique. This AUC value is considered asof high accuracy in diagnostic tests [35].

One limitation of this study is the marked difference betweenthe mean age of the two groups (healthy and ischemic subject).However, it must be pointed out to the progressive increase withage of the ischemic episodes incidence, both in number and dura-tion. Healthy people are by and large younger [36]. Thus, it shouldbe considered as an intrinsic and even expected behavior. Nonthe less, searching for an eventual possible correlation betweenthe suggested parameters and age for both populations, we runa modest linear regression analysis, computing the p-value fortesting the hypothesis of no correlation and the Pearson’s cor-relation coefficient r, with each one. This lead to the conclusionthat there is no significant correlation between the parametersmeasured over the QRS-loop and the patients’ age (|r| is close to0 and p-value is greater than 0.05). Only between the parame-ter STVM and age, in the healthy population, the p-value wentbelow the 0.05 level, but with a correlation |r| lower than 0.4,which was not verified in the ischemic group. Hence, we feel rea-sonably confident regarding the lack of significance of age in thismatter.

If we compare the results obtained in this study against thoserecently reported by A. Dehnavi et al. [37], it can be concluded thatour method has better performance in ischemic patient identifi-cation. Ischemia detection technique, based on 22 VCG featuresand a neural network classifier, obtains Sens = 70% and Spec = 86%.In contrast, our detection technique achieves Sens = 88.5% andSpec = 92.1% using only 8 VCG parameters and a simple linear clas-sifier.

Briefly, the proposed technique based on QRS-loop study couldbe used in addition to the traditional STVM analysis for a betteridentification of ischemic patients.

Acknowledgments

This study was supported by grants from Consejo Nacional deInvestigaciones Científicas y Técnicas (CONICET – PIP 538), Agencia

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acional de Promoción Científica y Tecnológica (ANPCYT – PICT-O027) and Universidad Nacional de San Juan (CICITCA-UNSJ I972)ll institutions from Argentina. The work of Dr. Arini was alsoupported by a grant from Fundación Florencio Fiorini, also fromrgentina. The authors would like to thank the anonymous review-rs for their helpful comments.

onflict of interest

The authors declare that they have no competing interests.

eferences

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