Electrocardiogram Signal Analysing

Delineation and Localization of ECG Component

Ouadi Beya

, Mohamad-Mazen Hittawe

, Nacira Zegadi

, Eric Fauvet

and Olivier Laligant

Le2i CNRS-UMR6306, University of Burgundy, 12 rue de la Fonderie, 71200 Le Creusot, France

CARDiags, 47 rue Andr

e Bollier, 69007 Lyon, France

Keywords:

Signal Processing, NFLS, ECG, QRS Complex, Waves Delineation.

Abstract:

In this paper, we develop a new approach based on nonlinear ﬁltering scheme (NLFS) on cardiac signal

to evaluate a robust single-lead electrocardiogram (ECG) delineation system and waves localization method

based on nonlinear ﬁltering approach. This system is built in two phases, in the ﬁrst phase, we proposed a

mathematical model for detecting ECG features like QRS complex peak, P and T-waves onsets and ends from

noise free of synthetic ECG signal. Later, we develop a theoretical model to obtain real approach for detecting

these features from real noisy ECG signals. Our method has been evaluated on electrocardiogram signals of

QT-MIT standard database, the QRS peak achieve sensitivity (Se) of 98.88 and a positive productivity (P+) of

98.43. For P-onset, P-end, T-end evaluations, this approach provides Sensitivity (Se) of 75.16, 71, and 90.7

respectively. Mean and standard deviation have been computed for differences between the automatic and

manual annotations.

1 INTRODUCTION

The analysis of the ECG is widely used for diagnosing

many cardiac diseases. Since most of the clinically

useful information in the ECG is found in the inter-

vals and amplitudes deﬁned by its signiﬁcant points

(wave peaks and boundaries), the development of ac-

curate, fast and robust methods for automatic and real

time ECG delineation is one of basic research ﬁled in

biomedical engineering domain. As a matter of fact,

QRS detection is necessary to determine the heart rate

and as a reference for beat alignment. ECG wave de-

lineation provides fundamental features (amplitudes

and intervals) to be used in automatic analysis system.

We can distinguish two main groups of algorithm in

the topic of ECG features extraction. They are QRS

detection algorithms and ECG waves delineation al-

gorithms. QRS complex is the most notable wave

in ECG signal which represents the duration of ven-

tricular depolarization. Its high amplitude and steep

slope make QRS detection easier than other waves.

Thus it is generally used as a reference within the

cardiac cycle. Many algorithms and approaches are

proposed to solve QRS detection problem. A compre-

hensive review and classiﬁcation these algorithms can

be found in (Kohler et al., 2002). Concerning P and

T-waves delineation, most algorithms start from pre-

deﬁned QRS complex and take a search window on

the left and right of QRS complex to detect P and T-

waves features onset, peak, and end respectively. Be-

cause of the low energy of P and T-waves, low signal

to noise ratio, amplitude and morphological variabil-

ity and possible overlapping of the P or T-wave with

QRS complex, it is more difﬁcult to delineate P and

T-waves than QRS complex. Fig. 1 shows ECG signal

interpretation with waves (P, T, U) and QRS complex

position, different interval and waves delineation are

indicated with the medical scale reference.

Figure 1: Normal ECG signal representation composed by

P wave, QRS complex, T wave, U wave. Different intervals

are shown with the medical scale reference.

In the literature, several approaches for P and T-

waves delineation can be found. Approach of Lie et

al (Lin et al., 2011) used Bayesian model, where as

Mehta et al (Chouhan and Mehta, 2008) proposed a

156

Beya, O., Hittawe, M-M., Zegadi, N., Fauvet, E. and Laligant, O.

Electrocardiogram Signal Analysing - Delineation and Localization of ECG Component.

DOI: 10.5220/0005684501560161

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 156-161

ISBN: 978-989-758-170-0

new method using Support Vector Machine approach,

likewise Martinez et al (Martinez et al., 2004) intro-

duced wavelet-based delineation algorithm. The val-

idation of the most recent algorithms for QRS detec-

tion is based on standard database, but most of P and

T-waves delineation approaches are not evaluated on

standard databases. Thus leads to problem in compar-

ing performance of these methods accurately. In this

work, we present a new approach to detect QRS com-

plex peak, P and T-waves onset/end points. The per-

formance is validated on the QT MIT database (La-

guna et al., 1997). This paper is organized as follows:

in Section 2, the concept of 1D Nonlinear Filtering

Scheme (1D NLFS) will be explained, then we will

present the mathematical model for features extrac-

tion from free of noise synthetic ECG signal in Sec-

tion 3. In Section 4, the real approach for features

detection derived from previous mathematical model

will be presented. Section 5 contains evaluation and

testing for this approach and a comparison with other

methods results. Conclusion and future works will be

presented in Section 6.

2 1D NON LINEAR FILTERING

SCHEME (NLFS)

In 1D NLFS presented in (Laligant and Truchetet,

2010), Laligant et al propose to achieve edge detec-

tion and noise reduction in one stage using nonlinear

derivative approach. This approach solved the prob-

lem of delocalization that appears in derivative ap-

proaches as well. In this scheme, author proposed to

localize the edge according to the sign of the transi-

tion slope. If the slope is positive, the edge will be lo-

calized after the transition; if the slope is negative, it

will be localized before. For decomposing the signal,

two detector ﬁlters are introduced F

(z) and F

−

(z)

regularized without zero in the center and are given

in Equation 1 :

(z) = 1 −z

−1

; F

−

(z) = −F(z − 1) (1)

Author used a nonlinear operator T as a threshold

for selecting the response. This scheme gives two sig-

nals given in equation 2: Y

which contains response

for positive slope edge points, and Y

−

which contains

negative slope edge points.

= T (F

(z)S(z)) ; Y

−

= −T (F

−

(z)S(z)) (2)

We can simplify this approach as follow. Two

derivative ﬁltering processes of the signal are applied

in two different directions. In each direction only

the transitions with the same slope sign are retained

as shown in the Fig. 2 and Fig. 3. The complexity

of edge detection problem is reduced to the half by

splitting the signal into two signals. This approach

reduces the edge detection complexity into half by

splitting the whole signal into two signals, each sig-

nal contains only one type of transitions; either with

positive or negative slope.

Figure 2: (A) Original signal S(t), (B) local positions of

increasing changes of S(t), (C) Y+ signal corresponds to

localization positions of increasing changes of S(t).

Figure 3: (A) Original signal S(t), (B) local positions of

decreasing changes of S(t), (C) Y- signal corresponds to the

positions of decreasing changes of S(t).

3 MATHEMATICAL MODEL

In this section, the mathematical model of our method

that exploits Y

, Y

−

signals to detect QRS peak, P

and T-waves Onset/End points will be presented. This

model will be built upon free of noise synthetic ECG

signal generated by a dynamical model method pre-

sented in (McSharry et al., 2003). Two main algo-

rithms will be discussed in this model, two main al-

gorithms, the ﬁrst one for QRS peak detection, and

the second for detecting onset/end of P and T-waves.

Electrocardiogram Signal Analysing - Delineation and Localization of ECG Component

157

3.1 QRS Complex Detection

The detectig of QRS peak is the starting point for any

ECG signal analysis process. So in this model, it will

be detected as follows: Applying 1D NLFS on ECG

signal to get Y

signal. Differentiating Y

signal to get

to di fY + signal. Then, di f Y + will be thresholded

to set all its values that are under threshold to zero,

whereas threshold value is 60% of the max value in

di fY + signal. The T di fY + signal is obtained, this

signal contains a series of Gaussian peaks, each one

of them corresponds to one QRS ﬁrst rising half (QR

segment). Linear search process within T di fY + will

be applied and the end of each peak gives the index

of QRS peak after shifting it by two samples forward.

Shifting forward is applied to compensate the part of

peaks eliminated in thresholding step. Fig. 4 shows

QRS peaks detections algorithm steps.

Figure 4: (a) Original ECG signal , (b) Y+: increasing

changes of ECG signal, (c) dif (Y+): differentiation of Y+

signal, (d) TdifY+: threshold of dif(Y+) signal in order to

obtain QRS complex localization.

3.2 P and T Waves Localization

QRS peak detection is used to determine boundaries

of search windows that should contains P,T-waves.

This search window falls on the right of QRS com-

plex from T-wave and on the left from P-wave. Af-

ter considering features of synthetic ECG model that

represents free of noise normal ECG signal, bound-

aries of P and T-waves search window will be given

by Equations (3) and (4). Once localized peaks R po-

sitions (Pos(R)) in QRS complex, we determine lim-

itations (start and end positions) of search windows

which should contain the airwaves P and T.

= Pos(R) − 40ms; P

end

= Pos(R) −

(3)

= Pos(R) + 40ms; T

end

= Pos(R) +

(4)

Where (P

, T

) and (P

end

, T

end

) represent respec-

tively beginnings and ends of search windows that we

want to detect onset/end points. RR represents the

distance between two adjacent QRS peaks. The con-

stant 40ms is determined via experiments on normal

synthetic ECG signal. For onset point detection, The

part of Y+ signal that falls between w0 , w1 will be

differentiated to give a feature signal that all its values

are zeros except Gaussian peak starts at the onset of

P or T-wave and end at the peak of P or T-wave. Lin-

ear search through this feature signal will be applied

to ﬁnd start of this Gaussian peak that corresponds to

onset point. For end point detection, the part of Y-

Figure 5: (a) P wave form, (b) Y+ signal corresponds to

the positions of increasing changes of P wave, (c) dif(Y+)

differentiation of Y+ signal.

signal that falls between w0 , w1 will be differenti-

ated to give a feature signal that all its values are ze-

ros except Gaussian peak starts at the peak and ends at

the end of P or T-wave. Linear search on this feature

signal to detect the end of this peak that corresponds

to end point will be applied. Fig. 5 and Fig. 6 show

an example for of P-wave onset/end detection steps

respectively. This mathematical model faces several

challenges in real ECG signals like noise and variable

morphologies of P and T- waves. These challenges

make it inapplicable on real ECG signals. So, start-

ing from this model we developed our approach for

detecting QRS peak, P and T-waves onset/end points

from real noisy ECG signals that will be presented in

next section.

4 REAL ECG FEATURES

EXTRACTION APPROACH

The backbone of this approach is the previous pre-

sented theoretical model. Real approach will show the

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

158

Figure 6: (a) P wave form, (b) Y- signal corresponds to the

positions of decreasing changes of P wave, (c) dif(Y-) dif-

ferentiation of Y- signal.

differences applied to overcome mathematical model

challenges. Besides, we will discuss new challenges

emerged in front of this approach, and what are the

best ways to overcome them. This approach starts by

QRS complex peak detection and proceeds for P and

T-waves delineation.

4.1 Onset/End Points of P and T-waves

Detection

Step 1: Preprocessing. The notable amplitude of

QRS peak and the steep slope that it has are main

two features that we are exploiting in our derivative

approach to build QRS peak detection algorithm. In

real ECG signal, sharp noise transitions could be ex-

isted in the signal. These sharp noise singularities

could be detected as QRS complex because they have

same features of high amplitude and steep slope. So,

moving average ﬁlter to eliminate such singularities

has been applied on ECG signal. Step 2: 1D NLFS.

After ﬁltering, 1D NLFS approach has been applied

on the ﬁltered ECG signal to get, Y+ signal. Step 3:

Obtaining Feature Signal. Applying differentiation

process on the Y+ signal to get difY+ signal. The

main purpose of this process is converting the rising

transition in this signal into Gaussian peaks. Then,

thresholding difY+ to get TdifY+ signal which set all

its values to zero except which are higher than Thresh

value determined in equation (5).

T hresh = 0, 6 ∗ max(di fY

) (5)

Step 4: Linear Search. Linear search process will be

applied on T di fY + signal, and register the index of

the end of each peak. Step 5: QRS Peak Detection.

After determining the end of each peak w0 , a narrow

search window around it on the original ECG signal

ECG (w0-5, w0+5) will be deﬁned. Within this nar-

row window, the local maximum value will be consid-

ered as QRS peak point. Step 6: Iteration. Repeat

Step 4 and 5 either up to detecting all required R peaks

in the signal or up to reach the end of the signal. Step

7: Deﬁning RR Line. Subtract the index of each R

peaks from previous R peak index value to ﬁnd RR

line which represents the heart rate.

4.2 P and T-wave Delineation in Real

ECG Signal

To give clear indication about how much this ap-

proach is robust to noise, we will delineate P and

T-waves in QT MIT database that contains certain

amount of noise without using any denoising proce-

dure in this part of our approach. The ﬁrst step in

detecting onset/end points is determining boundaries

of search window that contains P and T-waves. These

boundaries will be deﬁned exactly in the same way

used in the mathematical model and given in equa-

tions (3), (4). Obtaining feature signal by normal dif-

ferentiation step on Y+, Y- as we did in the mathemat-

ical model is not efﬁcient. The main two reasons are

the noise that comes from different sources to ECG

signal and the changes in amplitude of real ECG sig-

nal between main cardiac waves like P, QRS and T

waves that comes basically from muscles electrical

activity.

S(i) = Y (i + 8) −Y (i) (6)

The proposed solution for this problem is applying

differentiation process with a wide step about 32 ms

for parts of Y+, Y- that fall in region of interest i.e. be-

tween w0 , w1. The sampling rate in QT MIT database

ECG signals is 250 Hz (i.e: 32 ms) equals 8 samples

(sp). This differentiation step will reduce the effect

of noise and still gives a clear indication for onset/end

position within Y+, Y-. Equation (6) shows the fea-

ture signal obtained for detecting onset/end points: As

in the mathematical model, Y+ signal is used for on-

set points detection by considering the index of max-

imum value in S(i) feature signal as index for onset

point. Y- Signal is used for end points detection by

considering the index of minimum value in S(i) fea-

ture signal as index of end point after shifting it for-

ward with 8 samples.

4.3 Limits of NLFS Approach Applied

on Real ECG Signal

Like other methods and approaches in this ﬁeld

of research, this approach has its own limits like:

Electrocardiogram Signal Analysing - Delineation and Localization of ECG Component

159

1) Static thresholding used in QRS peak detec-

tion algorithm: ﬁxed threshold is used for this task,

and this ﬁxed thresholding may lead to false positive

detection in some rare cases of ECG signal such as

record 117 in MIH-BIT Arrhythmia database (Moody

and Mark, 2001) where height of T-wave amplitude

is more or less the same of QRS complex amplitude.

2) Static boundaries determination for search win-

dows to detect onset/end points of P or T-wave: In

some special cases of ECG signal P or T-wave is

overlapped with QRS complex. This overlapping

makes onset/end points fall out of search window.

3) Dependency of onset/end detection: P and T-waves

delineation is started from QRS peak deﬁned in pre-

vious step. So, if there is error in R peak detection

this will lead to error in P and T-waves delineation.

4) There are unusual morphologies for P and T-waves

could be existed in real ECG signals like inverted or

bi-phasic waves, presented approach unable to deal

with them.

5 EVALUATION AND TESTING

Because there is no golden rule to determine the

peak, onset and end of the ECG waves, the valida-

tion of such algorithms must be done using manu-

ally annotated databases like QT MIT database [5].

This database was developed for wave limits valida-

tion purposes and it provides cardiologist annotations

for at least 30 beats per recording, with marks in-

cluding QRS complexes, P and T waves peaks, on-

sets and ends. The QT DB also includes, for 11

out of its 105 records, an additional annotation per-

formed by a second cardiologist. Twelve records from

this database are selected to calculate the mean er-

ror (ME), Standard Deviation (SD) for difference be-

tween our approach detection and manual annotations

existed. Then we calculate the Sensitivity (Se) and the

Positive Predictivity (P+) values. Se=TP/(TP+FN),

and P+=TP/(TP+FP). Where TP represents True posi-

tive, FP represents False Positive, FN represents False

Negative parameters. For deciding to any one of these

choices our approach detection results belong, we will

consider standard deviation accepted error by cardiol-

ogist in any ECG automatic analysis system presented

in (Zywietz and Celikag, 1991).

Table. 1 shows standard deviation σ in (ms) of max-

imum accepted error for these points detection.

Table. 2 shows the result of applying our approach on

the subset of QTMIT database that contains about 300

annotated beats. We can note there is no result for

T-onset point, because it is not annotated manually

in QTMIT database. The visual observation shows

Table 1: Accepted tolerances for standard deviation ac-

cepted error σ (ms) by cardiologist in any ECG automatic

analysis.

Localization P on P

end

QRS on QRS end T end

σ(ms) 10.2 12.7 6.5 11.6 30.6

Table 2: Performance on subset of QT MIT database.

ME (ms) SD (ms) Se P+

onset

1.48 11.55 75.16 75.16

end

-1.747 13.57 71 71

peak

-3.251 2.487 98.43 98.88

end

-7.93 12.396 90.7 90.7

that the performance of our approach for T-onset point

should be close to T-end point performance.

For comparing results of this current work with

the best algorithms and approaches in this ﬁeld of re-

search, this approach gives good results in general as a

ﬁrst version of this approach. It gives less competitive

results for P-wave onset/end points, and gives good

performance for T-end. For QRS peak detection gives

good and competitive results. Despite it gives lower

values numerically, it is still promising approach for

following reasons: 1) It represents the ﬁrst initiative to

exploit 1D NLFS approach in signal decomposition.

2) It is single-lead based, fast and robust to noise ap-

proach which makes it convenient for real time ECG

analysis systems. 3) There are several enhancements

could be done on this work which can lead to enhance

its performance and decrease its limitations.

Table. 3 represents a comparison among this ap-

proach and best existing approaches in this ﬁeld

like wavelet- approach presented in (Martinez et al.,

2004), and Low Pass Differentiation approach pre-

sented in (Laguna et al., 1994), and Bayesian

detection-estimation algorithm presented in (Lin

et al., 2011). We can note that our approach gives

lower standard deviation error and lower Sensitivity

Positive. In Table. 3 we can note that our approach

gives lower standard deviation error and lower Sen-

sitivity, Positive Predictivity in the same time. The

main reason may be behind this paradox that other

approaches used wider accepted error than we consid-

ered in our approach for calculating Se, P+. Table. 3

shows QRS complex localization and delimitation by

applying NFLS approach on electrocardiogram sig-

nals from QTMIT database, these results are obtained

quickly and with high precision.

6 CONCLUSION

In this work, a new single-lead based fast and robust

to noise approach for ECG signal features detection is

presented. This algorithm could be exploited as a part

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160

Table 3: Results comparison of the proposed method with other methods.

Method Parameters P

end

QRS T

end

Proposed method Se (%) 75.16 71 98.88 90.7

P+(%) 75.16 71 98.88 90.7

m± s (ms) 1.48 ± 11.5 -1.7± 13.5 -3.2± 2.48 -7.9 ± 12.3

WT[4] Se (%) 98.87 98.75 99.92 99.77

P+(%) 91.03 91.03 99.88 97.79

m± s (ms) 2.0 ± 14.8 1.9± 12.8 NA -1.6 ± 18.1

LPD [10] Se (%) 97.70 97.70 NA 99.90

P+(%) 91.17 91.17 97.71

m± s (ms) 14 ± 13.3 -0.1± 12.3 13.5 ± 27.0

Bayes[2] Se (%) 99.6 99.6 NA 100

P+(%) NA NA NA

m± s (ms) 1.7 ± 10.8 2.5± 11.2 2.7 ± 13.5

of automatic analysis system for cardiac diagnosis.

We exploited for the ﬁrst time the concept of signal

decomposition into two sub signals, each one of them

contains half of the information. This decomposition

reduces problem complexity especially for onset/end

points detection algorithm. We started by proposing

theoretical model to show how we can extract impor-

tant information from free of noise synthetic ECG,

then this model is developed to present real approach

that can deal with real noisy ECG signals. This ap-

proach is a comprehensive algorithm existed nowa-

days, because most of current algorithms either for

QRS complex detection or for P and T-waves delin-

eation depending on predeﬁned QRS complex. Test-

ing has been done on twelve records from QTMIT

standard database to calculate mean error, standard

deviation, Se, P+. This approach gives good re-

sults for QRS peak, T-end points and less competitive

than other approaches for P-wave onset/end points.

The future works could be useful for improving per-

formance of this approach could be summarized as

follows: 1) Improving static thresholding procedure

used within QRS complex peak detection, to be dy-

namic and robust even for special rare clinical cases

could be faced in real ECG signals. 2) Using dynamic

and adaptive differentiation steps instead of static one

for obtaining feature signal from Y+, Y- can increase

the performance accuracy. 3) Adding denoising stage

beforeonset/end points detection will lead to improve

performance ofthis approach. 4) Generalizing this ap-

proach to be applicable to detect peak, onset and end

points from other type of signals.

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