Analysis of an Electrocardiographic Multilead System by Means of

Artiﬁcial Neural Networks

Study of Repolarization During Premature Ventricular Stimulation

Drago Torkar

and Pedro David Arini

2,3

Joˇzef Stefan Institute, Jamova Cesta 39, 1000 Ljubljana, Slovenia

Instituto de Ingenier´ıa Biom´edica, Facultad de Ingenier´ıa, Universidad de Buenos Aires, Buenos Aires, Argentina

Instituto Argentino de Matem´atica, ’Alberto P. Calder´on’ CONICET, Buenos Aires, Argentina

Keywords:

Electrocardiography, Transmural Dispersion, Mapping, ANN, Pacing.

Abstract:

The ventricular repolarization dispersion (VRD) has been shown to increase with premature stimulation.

Moreover, several differences between left ventricular and right ventricular, such as the anatomic properties

and ﬁbrillation threshold have been reported. However, few data exist regarding the inﬂuence of the site of

stimulation on modulation of VRD measure by electrocardiographic. In the present work, several ECG indices

of VRD, as a function of the coupling interval and the site of stimulation, were studied in an isolated heart

rabbit preparation (n=18), using ECG multilead (5 rows x 8 columns) system with Artiﬁcial Neural Networks.

In both ventricles, results have shown signiﬁcant decreases in early repolarization duration, while in the left

ventricle we have found signiﬁcant increases of transmural dispersion. Also, we have observed that when

the premature stimuli were applied to the left ventricle, the ventricular repolarization dispersion changes were

detected using only one preferential electrode (row1-column3). When stimuli were elicited at the right ventri-

cle, changes of VRD were detected by three electrodes (row3-column1, row2-column1 and row3-column8).

Finally, a different ventricular repolarization dispersion was found as a function of the site of stimulation.

1 INTRODUCTION

Heterogeneity of ventricular repolarization is a mea-

sure of nonhomogeneousrecovery of excitability dur-

ing the repolarization phase. This ventricular hetero-

geneity is mainly attributable to differences in activa-

tion times and action potential duration (APD) in dif-

ferent myocardium areas. The APDs differs not only

between cardiac cells of different ventricular layers

(Yan and Jack, 2003) but also between posterior and

anterior endocardial layers, apex and base (Noble and

Cohen, 1978), and left and right ventricles (Di Diego

et al., 1996).

Clinical and experimental studies haveshowna re-

lationship between ventricular repolarization disper-

sion (VRD) and severe ventricular arrhythmia and/or

sudden cardiac death (Surawicz, 1997) (Kuo et al.,

1983). In this way, changes in VRD values that

are higher than normal have been linked with an in-

creased risk of developing reentrant arrhythmias (Han

and Moe, 1964; Shimizu and Antzelevitch, 1998).

Some authors have shown that alterations in VRD

are correlated with changes in the total repolariza-

tion duration (T

) or T-wave width (Fuller et al.,

2000). Our study has also shown that T-wave widen-

ing can result from a differential shortening or length-

ening of the APD in both apex-base and transmural

(Arini et al., 2008). Moreover, the T-wave peak-to-

end (T

) interval has been suggested as a marker

of transmural repolarization dispersion (Antzelevitch

et al., 2007; Smetana et al., 2011), consequently the

interval between the J-point and the T-wave peak po-

sition has been considered as the full repolarization

of epicardium or early repolarization duration (E

The translation of these concepts to the standard ECG

is not straightforward, making it difﬁcult the inter-

pretation of the relationship between T-wave peak-to-

end and transmural dispersion in a clinical population

(Smetana et al., 2011).

In this regard, several investigations showed that

premature ventricular stimulation (PVS) produce a

signiﬁcantly increased of the VRD and that these

changes were markedly associated with an increase

in the induction of ventricular arrhythmias (Kuo

et al., 1985; Rosenbaum et al., 1991; Yuan et al.,

1996). Also, ventricular vulnerability, as evaluated

Torkar, D. and Arini, P.

Analysis of an Electrocardiographic Multilead System by Means of Artiﬁcial Neural Networks - Study of Repolarization During Premature Ventricular Stimulation.

DOI: 10.5220/0005663200340041

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 34-41

ISBN: 978-989-758-170-0

by the ventricular ﬁbrillation threshold technique, was

shown different when studied at the left ventricle (Lv)

or at the right ventricle (Rv). The left ventricular epi-

cardium presented higher ﬁbrillation threshold when

compared with left ventricular endocardium or both

epicardium and endocardium of right ventricular, re-

spectively (Horowitz et al., 1981).

Because a different ventricular ﬁbrillation thresh-

old and differences in the anatomic properties may

exist between both ventricles, we hypothesized that

there also would be differences in the E

, T

and

values depending on the site where premature

ventricular stimuli were elicited.

The aims of this work were to: (1) Determine

the preferential electrode, in a multilead ECG system,

to detect changes of ventricular repolarization disper-

sion using Artiﬁcial Neural Networks. (2) Evaluate

ECG indices associated to the ventricular repolariza-

tion dispersion depending on the site of pacing during

premature ventricular stimulation.

2 MATERIALS AND METHODS

2.1 Isolated Heart Rabbit Preparation

This study conformed to the Guide for the Care and

Use of Laboratory Animals published by the US Na-

tional Institutes of Health (NIH Publication No. 85-

23, revised 1996). To obtain isolated Langendoff-

perfused rabbit hearts, male New Zealand white rab-

bits of 2.8-3.8 Kg (n = 18) were heparinized (500

U/Kg IV) and anesthetized by the intramuscular in-

jection of a combination of lidocaine (5 mg/Kg) and

ketamine (35 mg/Kg). The rabbits were euthanized

by cervical dislocation. The chest was opened via a

median sternotomy and, immediately, the heart was

removed with scissors and immersed in cold Ty-

rode’s solution. After the remaining connective tis-

sue, lungs, and pericardium were removed, the heart

was placed in a vertical Langendorff device through

cannulation of the aorta. Time from chest opening

to cannulation of the aorta oscillated between 2 to 3

minutes. The heart was retrogradely perfused through

the aorta with Tyrode’s solution and immersed in a

tank ﬁlled with the same solution (Zabel et al., 1995).

The temperature of both solutions were maintained

at 38

± 0.5

C and bubbled with O

using a ﬂow of

700-900 ml/h at a pressure of 70 mmHg. To regulate

the ﬂow rate of the aortic perfusion, a variable speed

roller pump (Extracorporeal, 2102 Infusion Pump)

was used. Care was taken to ﬁx the hearts in the same

position by alignment of the left anterior descending

coronary artery (LAD) with the electrode matrix ref-

erence system on the tank (see Figure 1).

The composition of Tyrode’s solution was (in mM):

140 NaCl, 5 KCl, 1 MgCl

, 0.33 NaH

, 5 Hepes,

11.1 glucose and 2 CaCl

. The pH was adjusted to

7.4 using NaOH. The sinus node was destroyed by

applying radiofrequencyenergy through a customized

device.

The artiﬁcial pacemaker was a rectangular pulse

that had a 2 ms duration and twice the diastolic thresh-

old stimuli amplitude. In the premature ventricular

stimulation (PVS) experimental protocol, the bipo-

lar pacing electrodes made of Teﬂon-coated stainless-

steel wires were positioned in the middle of the base

of each ventricle, belowthe auricle appendage(Figure

1). To ensure stability in the preparation, the heart ac-

tivity was monitored for 30 min to determine that the

heart was arrhythmia-free, stable in amplitude, and

had no manifest ischemia. We used an In Vitro rab-

bit heart model because it provides advantages such

as a high level of experimental reproducibility, has a

greater throughput compared to complicated in vivo

models, provides a better evaluation over a range of

concentrations and different combinations of drugs to

be tested. In addition, it can be manipulated to mimic

clinical conditions, such as hypokalemia and brady-

cardia that support these comments. Also, it has been

well established that with PVS beats (Laurita et al.,

1998) a signiﬁcant increase in ventricular repolariza-

tion dispersion is induced.

2.2 Thorax Rabbit Model

The experimental model consisted of the In Vitro sys-

tem, which used a multiple recording system to obtain

the beat-to-beat electrical activity of isolated rabbit

heart. The PVS protocol used a circular tank (diam-

eter = 7 cm, height = 7 cm) that had 40 silver-silver

chloride electrodes (diameter = 2 mm) distributed ho-

mogeneously within an array of 5 rows and 8 columns

(see Figure 1). The distance between electrodes was

10 mm and the angular distance was 45

. The dimen-

sions of the tank simulated a rabbit’s thorax. Four ad-

ditional electrodes were allocated in an “Einthoven-

like” conﬁguration (Figure 1). Two of them were po-

sitioned on the base of the tank and the other two were

on the upper left and right side of the tank wall and

served as arm electrodes. The four electrodes were

designed to build the electrical reference by conﬁgur-

ing the Wilson Central Terminal.

2.3 Experimental Protocol

In this study VRD was modiﬁed by premature ventric-

ular stimulation. Due to heterogeneous distribution

Analysis of an Electrocardiographic Multilead System by Means of Artiﬁcial Neural Networks - Study of Repolarization During Premature

Ventricular Stimulation

Figure 1: ECG multilead system: 40-electrodes conﬁgu-

ration for electrocardiographic recording in the premature

ventricular stimulation protocol. Schematic view showing

the superior and frontal 5× 8 matrix electrodes, as well as

the standard lead foot (F), left arm (LA), right arm (RA),

and reference (Ref). Also are shown the positions of both

the stimulating electrodes located in the base of the Lv and

Rv, below the atrial appendages.

of APD lengthening induced by potassium-channel

blocking drugs (Zabel et al., 1997; Spear and Moore,

2000) or the heterogeneous shortening of APD caused

by the heterogeneous distribution of restitution kinet-

ics (Laurita et al., 1998), a real increase in VRD phe-

nomena can be obtained. Besides, it can be noted that

we measured the increase in VRD, not dispersion as

an absolute value, so our gold standard was the same

heart in the control condition in each experiment.

In the PVS protocol, the heart was stimulated from

the right ventricle (Rv) or left ventricle (Lv) at basal

frequency during a train (S1) of 49 beats. After that

train, at beat number 50

, a premature beat was gen-

erated at a coupling interval that corresponded to the

Effective Refractory Period (E

) plus 5 ms. In each

case, E

was estimated prior to the PVS application.

To estimate E

, premature coupling intervals (dis-

tance from the last beat to the premature stimulation

time) were diminished step by step at 5 ms until pe-

riod refractoriness was reached. We used the average

of 48

and 49

beats from each S1 as control. The

premature beat (50

) was elicited to generate VRD

paced either at Rv or Lv. During the protocol, the

heart was paced using an artiﬁcial pacemaker (DTU

101, Bloom Associates Ltd. Reading, PA, USA).

In the PVS protocol (n = 18), the hearts were

paced from the Rv (n = 9) or the Lv (n = 9) by stim-

uli trains at a basic cycle length of 430 ms for con-

trol condition. Then, single premature stimuli were

applied after a pulse train at a frequency equal to

+5 ms (167±7.2 ms for Rv stimulation (RVS) and

168±11.5 ms for Lv stimulation (LVS); p value =

NS).

0 500 1000 1500 2000 2500 3000

row 1

row 2

row 3

row 4

row 5

column

Time (ms)

Amplitude (mV)

0 500 1000 1500 2000 2500 3000

row 1

row 2

row 3

row 4

row 5

column

Time (ms)

Amplitude (mV)

Figure 2: 40 ECG recordings from the control situation (top

panel) and their respective E

+5 ms (bottom panel) during

PVS from the left ventricle. The stimulating electrode was

located at the base of the left ventricle.

2.4 Acquisition of ECGs Signals

ECG data were recorded using instrumentation am-

pliﬁers that had a gain factor of 1000 and a band-

width of 0.05-300 Hz. The signals were digitalized

at a sampling rate f

= 1 KHz and 12-bit resolution

using a digital acquisition board (Lab PC+, National

Instruments, Austin, TX, USA). When necessary, a

band-stop ﬁlter was used to remove 50-Hz. The base-

line movement was compensated using a cubic spline

(Meyer and Keiser, 1977) algorithm. All of the data

were acquired and monitored using customized soft-

ware made in C++.

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

2.5 Construction of Data Matrix

The ECGs from the ﬁrst row of leads were recorded

simultaneously, and the same procedure was applied

sequentially to the remaining rows. The i

beat was

selected in the ECG recordings of each r

row, r =

1, .. . , 5, obtaining the i

beat. After selecting and seg-

menting the i

beat from each row, a signal, x

c,r

(n),

n = 0, . . . , N − 1, was determined for each channel

characterized by the (c, r) pair, where c is the column

in the electrode matrix (c = 1, . . . , 8) and r is the row.

, being M = 5× 8 the number of register electrodes in

each experimental protocol, respectively. Expressing

that signal as a vector, x

c,r

, we obtain

c,r

= [x

c,r

(0), . . . , x

c,r

(N − 1)]

(1)

The ﬁve i

selected beats were aligned using the

QRS complex maximum upstroke slope. The beats

extend a time window composed of N samples cor-

responding to 400 ms, and include the repolariza-

tion phase. For each experimental condition (con-

trol and E

+5 ms), recordings were obtained from 40

ECG leads for the experimental protocol. Expressing

in matrix notation the selected segmented signals, X

(M × N), we obtain

X = [x

1,1

, . . . , x

L,1

, . . . , x

1,5

, . . . , x

L,5

]

(2)

From X, the ECG-derivedparameters were measured.

A matrix X characterize each experimental condition.

2.6 ECG Indices

The QRS ﬁducial points (QRS

and QRS

END

) and T-

wave location (T

END

, T

PEAK

) were obtained from the

ECG delineation system based on the Wavelet Trans-

form (Mendieta, 2012). Also, ECG indices have been

computed to describe the characteristics of VRD on

the electrocardiographic multilead system. For each

beat, we have computed as:

1) Ventricular depolarization index: the Q

in-

terval measured in milliseconds from the onset of the

Q wave to the offset of the S wave, has been calcu-

lated as;

= QRS

END

− QRS

(3)

2) Total ventricular repolarization duration index

(measured in milliseconds): the T

quantifying the

total ventricular repolarization time, has been com-

puted as;

= T

END

− QRS

END

(4)

3) Early repolarization duration index (measured

in milliseconds): the E

which several authors have

linked to the full repolarization of epicardium, has

been calculated as;

= T

PEAK

− QRS

END

(5)

4) T-wave peak-to-end interval index (measured in

milliseconds): the T

associated to transmural ven-

tricular repolarization (Antzelevitch et al., 2007), has

been computed as;

= T

END

− T

PEAK

(6)

2.7 Artiﬁcial Neural Network

The measured ECG indices depend on the premature

coupling interval between S1 pulse train and the pre-

mature beat. The question is, if it is possible to rec-

ognize the coupling interval from ECG values, or in

other words how successfully we can separate ECG

indices values corresponding to E

+5 ms coupling

interval from those corresponding to control stimu-

lation. Furthermore, we wanted to know the signals

from which electrodes can be most successfully clas-

siﬁed, since this would indicate the preferential elec-

trode positions in the tank (thorax rabbit model) most

suitable to perform measurements. There were 572

samples from 9 rabbit hearts (63.5 samples/heart) for

Lv stimulation (LVS) and 535 samples also from 9

rabbit hearts (59.4 samples/heart) for Rv stimulation

(RVS) available. The data was reasonably balanced

with 50.87% E

+5 ms and 49.13% control samples

in Lv stimulation data, and 48.22% E

+5 ms and

51.78% control samples in Rv stimulation data. The

LVS and RVS data were processed separately. Each

dataset was divided into 3 sets: the training set (60%),

the cross-validation set (20%) and the test set (20%).

Table 1: Lv stimulation (LVS) and Rv stimulation (RVS)

ANN parameters determined by genetic algorithm after 100

generations. Step size 1 refers to weights between input and

hidden layer, step size 2 refers to weights between hidden

and output layer.

LVS RVS

hidden layer neurons 25 21

step size 1 0.07 0.49

step size 2 0.41 0.44

momentum rate 0.70 0.70

The two class classiﬁcation procedure was per-

formed by employment of artiﬁcial neural networks

(ANN). We used a multi-layered perceptron with one

hidden layer and a backpropagation training algo-

rithm. This is a fully connected feed-forward only

ANN architecture with weighted neuron connections

from the input towards the output (Figure 3).

Analysis of an Electrocardiographic Multilead System by Means of Artiﬁcial Neural Networks - Study of Repolarization During Premature

Ventricular Stimulation

input layer output layer

hidden layer

inputs

outputs

control

E +5

Figure 3: ANN topology with input and output signals used

for classiﬁcation of ECG data.

During the training process these weights are au-

tomatically adjusted by a backpropagation algorithm

so that the difference between the actual and the de-

sired output is minimal. The momentum learning rule

and the logistic sigmoid transfer function were ap-

plied in all layers.The number of hidden layer neu-

rons, the learning step size and the momentum rate

were determined using optimization with genetic al-

gorithm (see Table 1).

The input data was randomized before training.

The genetic optimization took place throughout 100

generations. In every genetic iteration the ANN was

trained with 1000 epochs. The ANN outputs corre-

spond to an individual class and each one produces

a numbers between 0 and 1. The posterior deci-

sion function classiﬁed a particular sample into the

class which corresponded to the output that had big-

ger value. The classiﬁcation score was measured by

the standard measure: classiﬁcation accuracy (Eq. 7),

calculated from 4 standard quantities: true positive

(TP), true negative (TN), false positive (FP), false

negative (FN). Other standard measures like sensitiv-

ity, speciﬁcity and precision take the same values due

to a two class problem and the selected decision func-

tion. The whole training and the classiﬁcation of all

available data was repeated ten times and the results

were averaged.

CA =

(TP + TN)

(TP+ TN + FP+ FN)

(7)

Classiﬁcation accuracy was then calculated for

each measuring electrode and the ones with the best

scores were identiﬁed as preferential. The contour

maps were generated using these classiﬁcation scores

showing the preferential areas in the matrix of elec-

trodes.

2.8 ECG Recording Stability

To quantify the stability of ECG recordings we have

measured the coefﬁcient of variation (C

) parameter

(Rosner, 1994) for each feature, the Q

, T

, E

and T

. These variables were repeatedly measured

at each electrode of the multi-lead ECG recording

system every 20 min during an hour.

The C

is deﬁned as

× 100% (8)

and the σ

(variance within) is estimated as

∑

i=1

∑

j=1

−

)

n− k

(9)

For each variable evaluated (Q

, T

, E

and

) we assume that there are k groups of measure-

ments with n

measurements in the ith group. The jth

measurements in the ith group will be denoted by y

and n =

∑

i=1

. The term (y

−

) represents the de-

viation of an individual measurement from the group

mean for that measurement and is a clue of within

group variability.

2.9 Statistical Analysis

In order to quantify the discrepancy between the pa-

rameters’ distribution and the Gaussian distribution,

we have analyzed the normality of these values us-

ing the D’AgostinoPearson test. It has been observed

that the underlying variables’distribution was Gaus-

sian. Data were expressed as mean value ± stan-

dard deviation (SD). Comparison between ECG in-

dices were performed by means of paired or unpaired

Student t-test for normally distribution variables. Sig-

niﬁcance was considered at a value of p <0.05.

3 RESULTS

In some experiments, we have evaluated the C

for

each variable. The C

was < 2% for Q

, < 2% for

, < 3% for T

and < 3% for T

. So, we have

veriﬁed that the estimated variables have not shown

signiﬁcant statistical differences over the 1-h In Vitro

experiment.

It can be observed in Figure 2, a representative

example the 40 ECG recordings from the control sit-

uation and its respective premature ventricular stimu-

lation at E

+5 ms from the left ventricle.

The classiﬁcation results have shown that there is

one preferential electrode during LVS. It has been lo-

cated in row #1 and column #3 and so called as r1c3.

Moreover, we have observed three preferential elec-

trodes during RVS. These have been located in row

#3 and column #1 (denominated as r3c1); row #2 and

column #1 (so called as r2c1) and row #3 and column

#8 (denominated as r3c8).

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

Table 2: Mean values ± standard deviation of the experiments (n=8 during LVS and n=9 during RVS) showing all dispersion

ECG indices measured in control and in E

+5 ms. The values were computed in the preferential electrodes during stimulation

in Lv (n=8) and Rv (n=9), respectively.

control E

+5 ms. stimulation site preferential electrode p-value

76.50±7.33 100.13±85.50 Lv r1c3 0.0006

76.50±6.37 105.00±17.02 Rv r3c1 0.0002

73.00±10.17 101.33±18.61 Rv r2c1 0.0470

72.11±6.07 99.50± 20.35 Rv r3c8 0.0015

119.00±17.70 85.50±21.42 Lv r1c3 0.0018

114.14±17.09 107.50±8.54 Rv r3c1 NS

115.50±15.02 85.67±0.58 Rv r2c1 0.0202

122.63±18.30 94.40±26.82 Rv r3c8 0.0441

52.38±5.07 72.13± 21.26 Lv r1c3 0.0201

57.57±18.37 57.25±15.39 Rv r3c1 NS

58.00±7.62 79.67± 15.04 Rv r2c1 NS

54.75±9.62 56.00± 17.16 Rv r3c8 NS

171.38±18.15 157.63±16.75 Lv r1c3 0.0019

173.50±15.57 158.50±18.09 Rv r3c1 NS

173.50±10.79 165.33±15.01 Rv r2c1 NS

174.67±20.21 153.75±18.07 Rv r3c8 0.0409

Mean and standard deviation of Q

, T

, E

and T

indices during the control condition and dur-

ing LVS and RVS are presented in Table 2. It can be

observed that these results were computed for prefer-

ential leads in each kind of stimulation, LVS and RVS

respectively.

On the other hand, with the aim to localize the

spatial position of the preferential electrode in the 5 x

8 matrix electrodes, we have shown a colored contour

maps, as we can see in Figure 4.

We have tested the trained ANNs with all the

datasets available (Table 3). The lowest classiﬁca-

tion accuracy achieved was on the test set of LVS data

which is expected since the test set was not exposed

to ANN during training. The RVS data exhibit better

result on all datasets used. One reason for this might

be better quality of RVS measurements in compari-

son to LVS measurements. All available data has been

used to calculate the CA of every individual electrode.

The preferential electrodes exhibited 100% CA which

means that the signals from these electrodes can be

certainly distinguished one from another at all times

and thus they show the ventricular repolarization ac-

tivity most clearly. This is our most important result

obtained.

4 DISCUSSION

A total of 40 unipolar leads, in 18 isolated heart rabbit

preparation, were studied using Artiﬁcial Neural Net-

works with the aim to analyze: the preferential elec-

trode to detect VRD during premature stimuli (E

Table 3: Classiﬁcation accuracy (CA) for different data sets,

Lv stimulation (LVS) and Rv stimulation (RVS).

dataset CA LVS CA RVS

training set 0.83 0.86

cross-validation 0.85 0.89

test 0.78 0.87

all data 0.82 0.87

ms) and evaluate which ECG indices are dependent

on the site of pacing during premature ventricular

stimulation.

We have observed that when the premature stim-

uli were applied to the Lv, the VRD changes were

detected using only one preferential electrode (r1c3).

When stimuli were elicited at the Rv, changes of

VRD were detected by three electrodes (r3c1, r2c1

and r3c8). Moreover, we have observed that preferen-

tial electrode during stimulation of Lv is located op-

posite of this ventricle. At the same way the prefer-

ential electrodes during stimulation of Rv are located

exactly opposite of the right ventricle. It can be ob-

served these positions in Figure 1. Also, it is impor-

tant to highlight that all hearts were ﬁxed in the same

position, as we have describe in Section 2.1.

Otherwise, we have observed, in both Lv or Rv

premature stimulation (E

+5 ms), a statistical signif-

icant increase of Q

index duration (see Table 2).

We have concluded that depolarization cardiac phase

did not seem to be different when the stimuli were

elicited from either the Lv or Rv, because both ven-

tricles exhibited a similar response to the premature

ventricular stimulation.

Regarding the T

index, associated to transmu-

Analysis of an Electrocardiographic Multilead System by Means of Artiﬁcial Neural Networks - Study of Repolarization During Premature

Ventricular Stimulation

Figure 4: Contour maps of accuracies (CA) distributed over

the 5x8 matrix electrodes. Top panel: one preferential elec-

trode during Lv stimulation (LVS), located in row #1 and

column #3 (red area). Bottom panel: three preferential elec-

trodes during Rv stimulation (RVS), located in row #3 and

column #1, row #2 and column #1 and row #3 and column

#8 (red area).

ral ventricular repolarization dispersion, we have only

found statistically signiﬁcant increases during prema-

ture stimuli (E

+5 ms.) in the Lv (see Table 2).

Moreover, the E

index have shown statistically sig-

niﬁcant decreases in premature ventricular stimula-

tion of the Lv and the Rv (except in electrode r3c1,

see Table 2). Conversely, no signiﬁcant changes in

(only in r3c8 and with a p-value near to 0.05)

has been observed. We denoted that the T

index

did not change because the T

index increase and

decrease its values, simultaneously (see Table

2).

Artiﬁcial neural network is one of the machine

learning classiﬁcation tools which are most widely

used in biomedical applications due to good results

obtained (Dreiseitl and Ohno-Machado, 2002; Cai

and Jiang, 2014; Chen et al., 2015; Shaikhina et al.,

2015). It is a nonlinear non-parametric model which

can mimic from very simple to very complex prob-

lems. It possesses ability to implicitly detect com-

plex nonlinear relationships between dependent and

independent variables. The electrocardiographic mul-

tilead system seems to be an appropriate one to be

analysed by ANN. Of course, we could have picked

some other classiﬁcation algorithm but since there is

no way to select the most suitable method in advance

there should be performed a thorough comparison of

the methods which is beyond the scope of this paper

and is left for future work.

Moreover, the mechanism responsible for differ-

ent response by premature stimulation depending on

the site of pacing is not clearly explainable only

with the present results. There are anatomic differ-

ences between ventricles, such as the 3D structure or

the anisotropic properties linked with dissimilar wall

thickness and cardiac ﬁbers orientation. We have con-

cluded that all of these parameters might contribute to

the different results obtained between both ventricles.

Finally, the present results have shown that changes

of VRD during premature stimuli can be very well

captured by means of artiﬁcial neural networks in a

multilead ECG system

5 STUDY LIMITATIONS

No attempt was made to measure ventricular repolar-

ization dispersion on the epicardial surface or endo-

cardial muscle layers. We have limited our analysis to

ECG signals obtained from recording electrodes em-

bedded in the tank wall.

6 CONCLUSIONS

During premature ventricular stimulation we have ob-

served signiﬁcant decreases in early repolarization

duration for both ventricles, while in the Lv we have

observed signiﬁcant increases of transmural disper-

sion. Moreover, we havefound preferential electrodes

to detect VRD, when the premature ventricular stim-

uli were elicited from left or right ventricles.

ACKNOWLEDGEMENTS

This work were supported by Consejo Nacional de In-

vestigaciones Cient´ıﬁcas y T´ecnicas (CONICET) and

Slovenian Research Agency.

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

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Analysis of an Electrocardiographic Multilead System by Means of Artiﬁcial Neural Networks - Study of Repolarization During Premature

Ventricular Stimulation