Post on 07-Jan-2023
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: ravitia@uabc.edu.mx
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.
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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
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4 R.L. Avitia et al.: QRS complex duration enhancement as VLPs
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
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R.L. Avitia et al.: QRS complex duration enhancement as VLPs 5
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.
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6 R.L. Avitia et al.: QRS complex duration enhancement as VLPs
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
Beats considered in ensemble
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
Beats considered in ensemble
0 100 200 300 400
Noise level-20 dB
Noise level-10 dB
Noise level-15 dB
Beats considered in ensemble
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
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R.L. Avitia et al.: QRS complex duration enhancement as VLPs 7
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
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8 R.L. Avitia et al.: QRS complex duration enhancement as VLPs
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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