QRS complex duration enhancement as ventricular late potential indicator by signal-averaged ECGusing...

DOI 10.1515/bmt-2012-0120 Biomed Tech 2013; aop

Research article

Roberto L. Avitia* , Marco A. Reyna , Miguel E. Bravo-Zanoguera and Lucio A. Cetto

QRS complex duration enhancement as ventricular late potential indicator by signal-averaged ECG using time-amplitude alignments Abstract: Ventricular late potentials (VLPs) are small-

amplitude waves with a short duration that appear at the

end part of the QRS complex, making a QRS complex dura-

tion larger. The signal-averaged electrocardiography (ECG)

technique enhances VLPs and beats, assuming noise as

the only random variable. However, ECG signals are not

completely stationary and different elongations appear in

both time and amplitude in each beat. This research pro-

poses to use piecewise linear approximation to segment

each beat and performs the alignment of the beats using

the technique known as derivative dynamic time-warping

to have beats better aligned and consequently enhance

the presence of VLPs. We recorded high-resolution ECGs

(HRECGs) from 50 subjects in supine position with no

heart-stroke antecedents. VLPs were created synthetically

and added to the HRECGs. Two cases were evaluated: (i)

duration of the QRS complexes with VLPs without beats

alignment, and (ii) duration of QRS complexes with VLPs

using beats alignment in time and amplitude. Consider-

ing QRS duration as an indicative of VLP presence, results

show that when using beats alignment in time and ampli-

tude it is possible to reach a sensitivity of 0.96 and a speci-

ficity of 0.52, as opposed to 0.72 and 0.40, respectively,

when using only averaging without beats alignment in

time and amplitude.

Keywords: late potential indicators; QRS duration; time-

amplitude alignment.

*Corresponding author: Roberto L. Avitia , Full Professor and

Member of the Bioengineering and Environmental Health Research

Group, University Autonomous of Baja California, Benito Juarez

Blvd. s/n. Mexicali B.C., 21280 Mexico, Phone: + 52-6865664270,

E-mail: [email protected]

Marco A. Reyna and Miguel E. Bravo-Zanoguera: University

Autonomous of Baja California, Benito Juarez Blvd. s/n. Mexicali

B.C., 21280 Mexico

Lucio A. Cetto: Computational Biology Department, MathWorks Inc.,

3 Apple Hill Drive, Natick, MA 01760-2098, USA

Introduction

Sudden cardiac death (SCD) is the main cause of mortality

worldwide, causing approximately 325,000 deaths/year in

the USA [ 11 ]. The most common cause of SCD is a heart

rhythm disorder called ventricular fibrillation (VF). VF is

an electrical problem in which the electrical signals that

regulate the pumping of the ventricles of the heart become

chaotic. One of the most important causes in developing

VF is by sustained or malignant ventricular tachycardia

(VT). To identify this condition, high-resolution electro-

cardiography (HRECG) gives important information about

normal and abnormal electrical activity of the heart, such

as the presence of ventricular late potentials (VLPs). VLPs

are small-amplitude waves with short-time duration that

occur at the end part of the QRS complex and/or at the

beginning of the ST segment. These VLPs are precursors

to malignant VT and often occur in people who survive a

myocardial infarction [ 12 ].

Many VLP detection algorithms are based on quantify-

ing the QRS complex duration (QRSd) because this makes

QRS complex larger [ 2 , 6 , 7 ]. The main problem in detect-

ing VLPs is the inherent noise that hides them. To increase

detection accuracy, the signal-to-noise ratio (SNR) of the

HRECG must be increased; the better the enhancement

of the VLP presence, the longer the duration of QRS com-

plexes [ 3 ]. Signal-averaged ECG (SAECG) is a widely used

noise-reduction technique that combines multiple sub-

sequent ECG traces (i.e., ensemble) to produce a statisti-

cally significant average trace and reduce noise. SAECG

considers that the patterns of interest to be enhanced are

deterministic (i.e., HRECG components and VLPs if they

exist), and the remaining HRECG patterns are completely

random (i.e., noise). Therefore, the SNR of HRECG should

be increased. In reality, ECG is a quasi-deterministic signal

(i.e., quasi-periodic), so that the VLPs are not repeated

beat to beat accurately. In fact, we noted that VLPs could

be removed as the number of beats taken into averaging

is increased when the VLPs, which are immersed in the

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2 R.L. Avitia et al.: QRS complex duration enhancement as VLPs

beats, are not well aligned. Moreover, the physiological

nature of ECG signals shows a variable behavior from

beat-to-beat in lag, in duration, and in amplitude. Varia-

tions in time are noted by both heart rate variability and

by QRSd elongations. Variations in amplitude are shown

by attenuations or amplifications on the waveforms. In

other words, beats look lagged, shorter or longer in dura-

tion, and/or higher or lower in amplitude, so that every

beat may have different QRSd before averaging. Because

of this variability, beat-to-beat VLP repetition may occur at

different times and with different amplitudes on HRECG,

which vanish the potential goal of improving the SNR

when applying SAECG to the set of HRECGs.

In this work, we studied how to enhance VLPs when

QRS complexes show time and amplitude trace-wise mis-

alignment. We established that the prevalence of VLPs on

SAECG depends on good alignment of the beats before aver-

aging is applied. After beats alignment of R-peak delay, we

segmented HRECGs with a process called piecewise linear

approximation (PLA) [ 13 ]; then, alignment takes place on

segmented HERCGs using a technique known as deriva-

tive dynamic time-warping (DDTW). The DDTW technique

has been proven in making decisions on segmented ECG

signals, showing excellent curve aligning results [ 15 ].

Materials and methods We took HRECG records of 50 subjects aged between 20

and 40 years old with no known heart-stroke antecedents.

To prepare the signals for experiments on ECGs, a moving

average was first applied to remove high frequency noise

such as interference and muscle noise on HRECGs; this

permits to conserve important spectral components

(0 – 150 Hz) into the QRS. A CARDIAX PC-ECG system at

its maximum sampling rate (500 Hz) was used for record-

ing HRECGs. Nevertheless, VLPs can reach a frequency of

250 Hz, and the sampling rate is not enough to have at

least four samples in the worst case. Therefore, recorded

HRECGs were linear interpolated by two. A VLP dataset

was created according to VLP criteria described by the

American Heart Association and the European Society of

Cardiology [ 4 ]. First, a uniform random signal was gen-

erated containing frequencies from 0 to 1000 Hz. This

signal was multiplied by a normal distribution function

(i.e., Gaussian window), shifting it in time, scaling it in

amplitude (5 – 25 μ V) and varying the duration in time

(30 – 50 ms); this yielded different segments of Gaussian-

windowed uniform-random signal. For limiting the band

(from 40 to 250 Hz), a fifth order Butterworth filter was

used [ 1 ]. Thus, for controlled experimental purposes,

these segments would be meet the characteristics to be

used as synthetic VLPs. To apply and test the SAECG algo-

rithm with controlled interference, three different white

Gaussian noise levels were added to each HRECG, that is,

a SNR of -10 dB, -15 dB and -20 dB.

To have each HRECG separated into beats, we used a

method based on a difference operation process xd (n) = x (n)-

x (n-1), and then passed through an amplitude and time

threshold [ 14 ]. For purposes of building the ensembles, a

beat is determined for 150 samples before and 250 samples

after the R-peak. Every beat of the ensembles was corre-

lated with a beat template which was chosen visually. Then

only those that met a correlation higher than 0.9 with the

beat template were selected. Afterwards, averaging ECG

using same sample time index was applied to the high cor-

related beats in the ensemble. Also, SNR (Equation 1) and

QRSd variability (Equation 2) are assessed as the number

of beats in averaging increases.

signal

dB

noise

SNR 20 logAA

⎛ ⎞⎜ ⎟=⎝ ⎠

(1)

where A is the root mean square voltage amplitude of the

signal or the noise, and SNR dB

is the signal-to-noise ratio

in decibels.

2

2 1QRSd

( QRSd -QRSd)

-1

R

iis

R==∑

(2)

where the suffix i indicates the i th QRSd measured,

R indicates total number of beats to be averaging, QRSd

represents the mean of the set of QRSds and 2

QRSds is QRSd

variability in ms 2 .

After that, several VLPs were taken randomly from the

set of synthetic VLPs and added at the end part of every QRS

(i.e., QRSoffset) on HRECGs. The QRSoffset was localized

with the classical Pan and Tompkins algorithm [ 10 ]. Figure 1

shows the addition of the VLP immediately after the QRSoff-

set (i.e., end part of QRS complex). Then, beats alignment

of R-peak delay, PLA and DDTW algorithms are applied to

align each beat in time and amplitude comparing them

with the beat template. A QRSd > 114 ms is considered as an

indicator of VLP presence [ 9 ], and is therefore a reference

parameter to measure. Finally, the QRSd (as an indicative of

VLP presence) is computed and assessed on SAECG under

two experiments: (i) obtaining SAECG using same sample

time index of HRECGs + VLPs + noise on the ensemble (an

ensemble is composed of 380 high correlated beats), and (ii)

obtaining SAECG after beats alignment in time and ampli-

tude (i.e., alignment of R-peak delay, PLA and DDTW algo-

rithms) of HRECGs + VLPs + noise on the ensemble.



R.L. Avitia et al.: QRS complex duration enhancement as VLPs 3

Beats alignment in time and amplitude

Alignment of R-peak delay

In alignment of R-peak delay, the R-peak of every beat of

the ensemble is detected on the time axis. The time where

an R-peak occurs is compared with the time where the pivot

R-peak of the beat template occurs. All the beats in the

ensembles are shifted in time following the pivot R-peak;

thus, every beat is aligned with the beat template in each

ensemble. In other words, the time difference (delay) of

each R-peak from the template (pivot) indicates the magni-

tude in time to shift a beat to the right or left on the time axis

for reaching the alignment (see Figure 2 ). This procedure

guarantees the alignment of the R-peaks between the QRSs

on the ensemble, but does not ensure that the successive

sample points to the left and right of the R-peak of the QRSs

on the ensemble reach the best fit between them. This is

because the non-stationary nature of ECG signals makes

their duration (e.g., QRS duration) and their amplitude

variable. These variations prevent the SAECG algorithm to

reach the best SNR, because VLPs do not match completely

beat-to-beat on the ECG and it would be considered as noise

by the SAECG according to Equation 3.

1

( ) ( ) / R ( ) n( ) / RR

ii

x t x t s t t=

= = +∑

(3)

where x̅ is the averaged signal, x i ( t ) is the i th beat, and

R is the total number of beats. Or in general if signal s ( t ) (i.e., the HRECG beat + VLP) is stationary will remain and

only the noise n ( t ) will be reduced as the number of beats

R increases.

800

700R

Q

Synthetic VLP added

QRSoffsetS

600

500

400

300

200Am

plitu

de (m

V)

100

-100

-200480 490 500 510 520

Samples530 540 550

0

Figure 1 QRS and VLP addition. Once the QRSoffset is found on the

beat, the synthetic VLP is added immediately at this point.

QRS1A

B

QRSn------

2 3 1

4 52

17 215

12 07

8 3

8

Pivot R-peak

0

8

7

10

4

2

1

3

117

2 3 1 8 4

2 4 5 0 2

5

387

17 21 8 1

7 12

10 11

0 7 3

Figure 2 Illustration of aligning R-peak delay. (A) Array (ensemble

section) showing adjacent values (columns) to R-peaks (circled)

of each QRS (rows). (B) Same ensemble described in (A) but after

aligning R-peak delays. The arrows indicate the shifts to the left or

right of the R-peak taken as a pivot (indicated in A as R-peak pivot).

Alignment using DDTW and PLA

The best possible alignment of the beats (i.e., HRECGs +

VLP) forming an ensemble avoids that VLPs are confused

as random components (noise) and hence be attenuated by

the averaging algorithm. Therefore, misalignments in time

and amplitude occurring beat-to-beat regarding a template

are corrected using the DDTW algorithm. This algorithm is

based on the classical DTW (dynamic time-warping) algo-

rithm, which presents some limitations. DTW finds the

similarity between two sequences (signals) and looks for

the best alignment, which is generally known as warping

path. This algorithm contracts or expands (warping) the

time axis of one of the sequences based on the ampli-

tude distance between them [ 15 ]. In some cases, it can

produce some misalignments, for instance, when multi-

ple points of one sequence correspond to only one point

in the matching sequence (template sequence), or when

the two sequences strongly vary in amplitude. Each beat is

considered as a sequence of n samples x = [ x (1), x (2) … x (n)]

and the beat template is a sequence of m samples y = [ y (1),

y (2) … y (m)]. DTW builds an n × m local distance matrix d ,

in which each element represents the distance between the

i -th element of x ( i ) and the j th element of y ( j ). Then, a new

matrix θ (cumulative distance of the adjacent elements) is

introduced, with Equation 4:

θ ( j,i ) = d ( j,i ) + min[ θ ( j -1, i -1), ( j,i -1), θ ( j -1, i )] (4)

This matrix contains the paths between neighbor ele-

ments, as the sum between the local distance d ( j,i ) and the




minimum of the total distances of the neighbor elements.

The warping path W , defined by Equation 5, is a contigu-

ous set of matrix elements that defines mapping between

x and y . The k -element of W is defined as w k = ( i,j) k :

W = w 1 , w

2 , … w k max(n,m) ≤ K < n + m -1 (5)

The warping path generally undergoes several con-

straints: among them, the requirement for the warping

path to start and finish in diagonally opposite corner

cells of the matrix, restriction to the number of allowable

steps in the warping path to adjacent cells and mono-

tonic in time. Among all the warping paths that satisfy

the above conditions, it is necessary to choose the

warping path that minimizes the cost, as indicated in

Equation 6, where K is used to compensate the length of

warping paths.

1

1DTW( , ) min

K

kk

x y wK =

⎧ ⎫⎪ ⎪⎨ ⎬=⎪ ⎪⎩ ⎭

∑

(6)

Error e(i)

y(i)=a*i+b

y(i)-y(1)

x(j)-x(1)1 ji

x(i)

Figure 3 Piecewise linear approximation (PLA). The error for a

segment. This error is an empirical value that better approximates

ECG traces using lines.

10,000

5000

-50000 50 100 150 200

A HRECG beatA

B A HRECG beat segmented using PLA

250 300 400350

0 50 100 150 200 250 300 400350

0

5000

-5000

-10,000

0

Figure 4 Template after PLA application. PLA of a beat taken as beat template.

The DDTW algorithm solves the limitations that arise

in the DTW because it aligns (warping) sequences based

on waveforms patterns and not on the distances between

amplitudes. Now, as indicated in Equation 7, the distance

between two sequences represented by straight lines is

calculated considering the square of the difference of the

estimated derivatives of x and y [ 15 ].

( ) ( )( )

( ) ( )( )

-1 1 -1

2

-1 1 -1

- - / 2( , )

2

- - / 2- , 1

2

i i i i

i i i i

x x x xd x y

y y y yi n

+

+

⎡ +=⎢

⎣

⎤+⟨ ⟨⎥

⎦

(7)

The PLA algorithm can adaptively represent any

signal through straight lines. This permits to approximate

the wave components of the beats (e.g., P, QRS and T )

by means of a series of straight line segments [ 8 ]. These

straight lines allow the use of the DDTW algorithm, which

align each segmented beat with the segmented beat tem-

plate in both time and amplitude. To make possible PLA

application, a beat was considered as a vector which is the

amplitude at time i: x = [ x (1), … , x (n)], where x ( i ) (1 ≤ i ≤ n).

The first segment of the series was composed by the two

first samples of x . This segment was approximated with

a straight line connecting the first sample with the last

sample (in this case with the second sample) and the error

e was computed. Obviously, the computation of the error

generated by the first approximation will be zero, so that

should be discouraged and proceed to include the next

sample. As long as the new line approximates the origi-

nal segment x with an acceptable error e , more samples

s are added to the segment. Figure 3 shows how the error




is computed, where j samples are approximated by the

straight line y ( i ) = ai + b . The error e for sample i (1 ≤ i ≤ j ) should never exceed an empirical threshold computed by

Equation 8.

2

( )- ( )( )

1

x i y ie ia

= <∈+

(8)

More samples are added to the segment until Equation

8 (i.e., the error) exceeds the threshold ∈ . Then, we started

shrinking the segment in order to obtain a segment which

does not exceed ∈ . The new end-point of the segment is

point i for which e ( i ) is maximum. If the error on the new

segment remains below ∈ , the line segment to approxi-

mate a part of the beat is found.

The new segment will start at the end-point of the pre-

vious segment. However, if the error is still not below ∈ ,

we shrink the segment again, until error represented by

Equation 8 remains below ∈ . A complete segmented beat

consists of a sequence of lines which can be displayed.

One line can be represented as a combination of slope

and a horizontal length, which enables us to describe the

segmented beat in terms of segment lines as illustrated

in Figure 4 , where a segmented beat template was repre-

sented using PLA, that is, using lines defined by a slope

and number of samples taken into account by these lines.

Once we have segmented beats and template, we proceed

to the alignment.

Evaluation and analysis

Two groups from the 50 HRECGs were built: 25 HRECGs plus

synthetic VLPs added to the beat on the HRECG immediately

after QRSoffset, and 25 HRECGs without synthetic VLPs. The

procedure described in Figure 5 has been applied to all 50

HRECGs. The column marked with an A in the flow diagram

Addition of Gaussiannoise to every HRECG

trace

Addition of VLPsimmediately after

QRSoffset

Separation of beatsfrom HRECGs

Selection ofbeat-template

by visual criteria

Alignmentof R-peaks

delayed

Creation of theensemble with high

correlated beats(higher than 0.9)

Application ofaverage of the

ensemble (samesample index)

Compute QRSd asindicator of VLP

presence

Compute QRSd asindicator of VLP

presence

BA

Application ofaveraging ensemble

(according to aligning)

Alignment usingPLA and DDTW

Detection the end ofevery QRS complex

(QRSoffset)

Figure 5 SAECG including beat alignment in time and amplitude

process. (A) Traditional SAECG, (B) amplitude and time alignment is

applied before averaging.

Minimum warping path (red)A B C2

6000

4000

2000

-2000

-4000

-6000

0

8000

6000

4000

2000

-2000

-4000

-8000

-6000

0

468

101214

Am

plitu

de (m

V)

Am

plitu

de (m

V)

16182022

2 4 6 8 10 12 14 16 18 0 50 100 150 200 250Samples

300 400350 0 50 100 150 200 250Samples

300 400350Segmented-beat template

Seg

men

ted-

beat

to b

e al

igne

d

Two segmented beat aligned (black) Amplitude of two beats aligned (black)

Figure 6 Applying time and amplitude alignment. (A) Distance matrix using beats segmented, (B) segmented beats aligned using a

template (red), a beat to be aligned (blue) and beat aligned (black) and (C) beat aligned showing samples.




12030

25

20

15

10

0

5

QRSd of beats average in ensembleA BSignal-to-noise ratio comparative

115110105100

95

Tim

e (m

s)

SN

R (d

B)

908580

700 100 200 300 400

Beats considered in ensemble

0 100 200 300 400

Noise level-20 dB

Noise level-10 dB

Noise level-15 dB


75

Figure 7 QRSd variability after traditional SAECG application. (A) QRSd every 20 beats averaged. (B) SNR tendency increases as the number

of beats averaged increased too.

is considered traditional SAECG, and the other marked with

a B is the new method proposed to make beat alignment

in time and amplitude previous to the averaging process.

When averaging is applied the SNR should increase. In other

words, the quality of the signal increases when the number

of iterations on the ensemble average is higher (procedure

A). By contrast, when beat alignment is applied using a beat

template, QRSd variability should decrease as the number

of beats taken into account increases (procedure B). Once

chosen the best 380 beats with a correlation coefficient > 0.9

compared with the beat template, two assessments were

applied described as the next steps:

120

3025

4035

45

201510

05

QRSd of beats average in ensembleA B Signal-to-noise ratio comparative

115110105100

95

Tim

e (m

s)

SN

R (d

B)

908580

700 100 200 300 400


0 100 200 300 400

Noise level-20 dB

Noise level-10 dB

Noise level-15 dB


75

Figure 8 QRSd variability after beat alignment in time and amplitude. Applying time and amplitude alignment is possible to reach (A) good

QRSd variations and (B) SNR levels using fewer beats.

Table 1 QRSd variability after traditional SAECG application a .

QRS d variability (ms 2 ): traditional SAECG

Beats averaged in ensemble

Noise added (dB) 100 200 300 380

-20 91.6 85.4 59.8 57.3

-15 69.4 55.3 46.3 42.8

-10 52.2 46.6 39.6 36.1

a QRSd variability decreases as the number of beats averaged is

increased.

1. Computation of QRSd variability every 20 beats to

make a total of 380, averaging and computing SNR

and QRSd as indicators of VLP presence.

2. Beats alignment in time and amplitude (alignment

of R-peak delay, PLA and DDTW), computation of

QRSd variability every 20 beats to make a total of 380.

Averaging and computing SNR and QRSd as indicators

of VLP presence.

The block diagram shown in Figure 5 demonstrates the addi-

tion of VLP at the end of each QRS complex. Procedure A is

the evaluation of conventional SAECG when a VLP is pre-

viously added. Procedure B is the evaluation of beat align-

ment in amplitude and time prior to applying averaging.

Once a synthetic VLP has been added to a HRECG, a

QRSd detection of < 100 ms is considered as a characteris-

tic with no risk of VLP (false negative, FN) [ 5 ], and a QRSd

detection > 114 ms is considered as a likely presence of VLP

(true positive, TP). By contrast, when the HRECG does not

contain simulated VLP, and a QRSd detection is < 100 ms,

it is considered as a characteristic with no risk of VLP (true

negative, TN); and a QRSd computation > 114 ms is consid-

ered as a likely presence of VLP (false positive, FP). Sen-

sitivity was defined as the number of TP results divided




Traditional averaging with no alingments130125120115110

QR

Sd

(ms)

105100

9590

800 10 20 30 40

HRECG trace50 60

TP=18FP=15FN=7TN=1085

Figure 9 QRSd evaluation under traditional averaging. Application of

traditional averaging to each HRECG using only high correlated beats

ensemble produced a sensitivity of 0.72 and specificity of 0.40.

Table 2 QRSd variability after beat alignment in time and

amplitude a .

QRSd variability (ms 2 ): beat alignment in time and amplitude

Beats averaged in ensemble

Noise added (dB) 100 200 300 380

-20 29.6 26.1 21.2 12.9

-15 20.3 12.2 10.2 9.2

-10 14.4 8.41 5.7 4.4

a QRSd variability decreases as the number of beats averaged and

aligned is increased.

by the number of TP plus the number of FN results. Speci-

ficity was defined as the number of TN results divided by

the number of TN plus FP results. The positive predictive

value was defined as the number of TP results divided by

the number of TP plus the number of FPs.

Results and discussion Each beat segmented using PLA has a distance matrix

alignment (warping path) much smaller than if it were

made with all samples of the signals (i.e., lines represent-

ing beats). The DTW alignment technique produces an

alignment based on the slopes of the lines generated. In

Figure 6 , using gray scale levels, we show distance matrix

between beat template and the beat to be aligned.

The first evaluation is to verify that applying only

delay alignment when the number of beats increases

also the level of SNR level increases. By contrast, as the

number of beats increases the QRS duration of complex

tends to a single value. Figure 7 shows this effect when all

beats selected have a correlation > 0.9 and without using

any type of alignment.

Variations in QRS duration are larger as the noise

level added increases. An acceptable SNR is reached at

approximately 350 beats (i.e., a SNR of 25 dB). Table 1

shows results of changes in QRSd when three different

noise levels have been added. One can see that QRS com-

plexes have different lengths even when the number of

beats taken in the average is high.

When two beats are different in duration, DDTW and

PLA can be used to make beats alignments in time and

amplitude a beat template as reference. As the number

of aligned beats increases the duration of the final

complex in averaging tends to stabilize at a single value.

The SNR trend continues to increase as the number of

beats in the average increases. An acceptable SNR (i.e.,

Averaging after beats alingment in time and amplitude

0 10 20 30 40HRECG trace

50 60

TP=24FP=12FN=1TN=13

130125120115110

QR

Sd

(ms)

105100

9590

8085

Figure 10 QRSd evaluation after beat alignment in time and

amplitude. Application of beats alignment in time and amplitude

produced a sensitivity of 0.96 and a specificity of 0.52.

25 dB) is achieved at approximately 180 beats as shown

in Figure 8 .

Although the added noise level raises variations

of the QRSd, when we apply beats alignment in time

and amplitude (i.e., alignment R-peak delay, DDTW and

PLA) the QRSd achieves a lower variation than achieved

in simple delay alignment, as shown in Table 2 . The 50

HRECGs were split into two groups: 25 HRECGs plus syn-

thetic VLPs and 25 HRECGs without synthetic VLPs. Two

tests were performed for detecting QRS complex duration

as indicator of VLP presence (i.e., > 114 ms). Every HRECG

has approximately 380 beats and applying SAECG noise is

significantly reduced. Figure 9 shows every QRSd evalua-

tion in every signal-averaged HRECG when synthetic VLP

was added.

When beats alignment in time and amplitude are

applied to each HRECG and after SAECG, results show that

capacity to detect contaminated records (TP) and capacity

to detect no contaminated records (TN) are increased, as




shown in Figure 10 , where QRSd evaluation is represented

with a gray diamond.

Conclusions The alignment algorithm developed based on DDTW and

PLA provides similar results in noise reduction compared

with the traditional method based on high correlation

for the same number of beats. For fewer beats, however,

it reaches lower noise levels thus excluding the need to

reject as much as the traditional method. We considered

here that a QRSd longer than 114 ms is indicative of SCD

risk. After simulated VLP was added to HRECGs, we con-

sidered half of the records as contaminated records or

people with SCD risk. Once time and amplitude align-

ment is applied, sensitivity and specificity was increased

producing better risk and no risk classification according

to QRSd. We conclude that applying amplitude and time

alignment is necessary before SAECG application when

QRSd is a parameter related to SCD risk, and not only a

high correlation coefficient between beats. This technique

constructed that a VLP (if it exists) was enhanced and

prevailed as long as the number of averaged heartbeats

are increased. In the future, it is recommended to use an

extensive HRECG database in order to compare QRSd as a

parameter of VLP detection, as well as more different VLP

amplitudes and durations.

Acknowledgments: We would like to acknowledge the

sponsorship granted by the Fifteenth Internal Call for

Research Projects for New Researchers 105/6/N/41/15-1908

from the Autonomous University of Baja California.

Conflict of interest statement : The authors declare that

they have no conflict of interests.

Received September 10, 2012; accepted January 28, 2013

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