MEASURING P-WAVE MORPHOLOGICAL VARIABILITY FOR

AF-PRONE PATIENTS IDENTIFICATION

Valeria Villani

∗,†

, Antonio Fasano

∗

, Luca Vollero

∗

, Federica Censi

†

and Giuseppe Boriani

‡

∗

Universit

a Campus Bio-Medico di Roma, Roma, Italy

†

Department of Technology and Health, Italian National Institute of Health, Roma, Italy

‡

Institute of Cardiology, University of Bologna and Azienda Ospedaliera S. Orsola-Malpighi, Bologna, Italy

Keywords:

ECG, Atrial ﬁbrillation, P-wave variability.

Abstract:

Atrial ﬁbrillation is the most common arrhythmia encountered in clinical practice. Abnormal P-waves have

been observed in patients prone to AF and the analysis of P-waves from surface electrocardiogram has been

extensively used to identify patients prone to atrial arrhythmias. Measuring the temporal variability of P-

waves, i.e., the variation over time of morphological characteristics of single P-waves, may represent a useful

method for characterizing and predicting AF cases. In this paper, we propose a method for the statistical

analysis of P-waves variability. It is based on the evaluation of the empirical distribution function of differences

energy among normalized P-waves. The proposed method seems promising for capturing atrial anomalies and

identifying patients prone to AF.

1 INTRODUCTION

Atrial ﬁbrillation (AF) is the most common arrhyth-

mia encountered in clinical practice (about 4.5 million

people in the European Union): its prevalence is esti-

mated between 0.4% and 1% in the whole population

and increases with age (5% for patients older than 65

years and 8% for those older than 80 years). Although

it is not a lethal disease, AF may increase mortality

up to 2-fold, primarily because of embolic stroke. In-

deed, the lack of coordinated atrial contraction leads

to unusual ﬂuid ﬂow states through the atrium. These

could favor the formation of thrombus at risk to em-

bolize, especially after return to normal sinus rhythm.

When normal cardiac impulse travels through

atrial myocardium, surface electrocardiogram (ECG)

recordings show the P-wave. If atrial depolarization

patterns differ from normal ones, P-waves may ap-

pear prolonged and highly variable. Indeed, slowed

conduction velocity in several atrial regions together

with different cell refractory periods are believed to

be the electrophysiological conditions provoking and

maintaining AF (Platonov et al., 2000).

Abnormal P-waves have been observed in AF-

prone patients and the analysis of P-waves from

surface ECG has been extensively used to identify

patients prone to atrial arrhythmias, especially AF

(Dilaveris et al., 1998; Darbar et al., 2002; Michelucci

et al., 2002; Dilaveris and Gialafos, 2001).

In this context, P-wave analysis is usually per-

formed following both the conventional 12-leads

ECG approach and the three bipolar orthogonal leads

one, used to determine the P-wave vector magnitude

(Clavier et al., 2002; Klein et al., 1995; Jordaens et al.,

1998; Dilaveris and Gialafos, 2002). Given the re-

latively low P-wave amplitude with respect to back-

ground noise, both approaches use signal averaging

techniques to obtain a P-wave template. The analy-

sis of P-wave template turned out to be useful in dis-

criminating patients at risk of developing AF or with

paroxymal AF. A typically considered parameter is

the P-wave duration.

According to literature, the classical approach fol-

lowed to study the relation between P-wave charac-

teristics and AF is the analysis of P-wave templates.

Nevertheless, following an approach similar to that

used for T-waves in the analysis of ventricular repo-

larization (Pueyo et al., 2009), it could be worth in-

vestigating P-waves variability, i.e., the variation over

time of morphological characteristics of P-waves. In-

deed, as shown in Fig. 1 and Fig. 2, variability of P-

waves morphology usually appears to be signiﬁcantly

higher in AF patients than in healthy controls. Hence,

such an approach seems to be more suitable for in-

vestigating the complicated electrophysiological con-

ditions believed to provoke and maintain AF.

481

Villani V., Fasano A., Vollero L., Censi F. and Boriani G..

MEASURING P-WAVE MORPHOLOGICAL VARIABILITY FOR AF-PRONE PATIENTS IDENTIFICATION.

DOI: 10.5220/0003164204810484

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 481-484

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

0 50 100 150

−0.2

−0.15

−0.1

−0.05

0.05

0.1

Time [ms]

Amplitude [mV]

Figure 1: Butterﬂy plot of P-waves belonging to a patient

prone to AF.

0 50 100 150

−0.2

−0.15

−0.1

−0.05

0.05

0.1

Time [ms]

Amplitude [mV]

Figure 2: Butterﬂy plot of P-waves belonging to a healthy

subject.

The aim of this work is to propose an approach to

measure P-waves variability that could be effective in

identifying AF prone patients.

2 METHOD

This section describes the strategy we have develo-

ped in order to measure P-waves variability within an

ECG tracing of cardiac cycles.

2.1 Study Population

The study population consisted of 37 subjects, di-

vided into 2 groups: 21 patients had a persistent

form of atrial ﬁbrillation and underwent electrical car-

dioversion (AF group); 16 subjects had no history of

AF and have been considered as controls in this paper

(control group). The AF group consists of 10 patients,

who experienced at least another documented episode

of AF within 3 months after cardioversion (AF relapse

group), and 11 patients, who did not experience any

documented AF episodes after cardioversion (no-AF

relapse group). ECG was recorded for 5 minutes us-

ing a high resolution mapping system (Biosemi Ac-

tiveTwo, Amsterdam, Netherlands) with a sampling

frequency of 2048Hz, a resolution of 24-bit (about

30nV LSB), and a bandwidth from DC to 400Hz. For

AF patients, ECG was recorded after successful car-

dioversion.

2.2 Pre-processing of P-waves

P-waves were ﬁrst isolated according to the method

proposed in a previous paper (Censi et al., 2007). Af-

ter the detection of each R-wave, P-waves were ex-

tracted in a 200ms-long window starting from 300 ms

before the R-wave. Then, in order to remove base-

line wander, a beat-by-beat linear piecewise interpo-

lation was performed, selecting ﬁducial points from

TP and PQ tracks of each beat for linear interpola-

tion. Finally, ectopic atrial signals or P-waves with

excessive noise were excluded by template matching

of each P-wave as described in (Censi et al., 2007).

The extracted P-waves are ﬁnite length sampled sig-

nals available in the following as vectors having the

same size. P-waves from ECG II lead were consid-

ered.

2.3 Quantiﬁcation of the Energy of

P-waves Differences

P-waves selected during the above mentioned pre-

processing phase are analyzed in order to deﬁne and

characterize statistical indicators of AF phenomena

based on P-waves morphological variability.

We denote by p

the i-th segmented P-wave of the

ECG tracing under analysis. Let us assume to have

N of such waves each L samples long. In order to

emphasize the morphological differences instead of

absolute differences among waves, a unit-norm nor-

malization is applied to each of them:

||p

Denote by ε

i, j

the energy of the difference bet-

ween

and

i, j

= ||

−

In the following we consider the squared Eu-

clidean norm:

i, j

∑

k=1



ˆp

i,k

− ˆp

j,k



where ˆp

i,k

is the k-th component of the vector

However other norms revealing speciﬁc aspects of

waves can be used.

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

482

The empirical cumulative distribution function of

the differences energy among normalized P-waves is

hence obtained as:

(ε) =

N (N − 1)

∑

i< j

{ε

i, j

≤ε}

where 1

{·}

denotes the indicator function of the set

within brackets.

Although the empirical cumulative distribution

function is rich of information about waves variabi-

lity, the availability of descriptive parameters is also

of interest. In the following we consider the sample

mean and the sample variance of the differences ener-

gies as indicators:

µ =

N (N − 1)

∑

i< j

i, j

N (N − 1)− 2

∑

i< j

[ε

i, j

− µ]

3 RESULTS

Before ECG acquisition, AF patients underwent elec-

trical cardioversion. In Fig.s 3, 4 and 5 we report the

empirical cumulative distribution functions pertain-

ing to controls, no-AF relapse group, and AF-relapse

group, respectively. Each curve corresponds to a pa-

tient.

Fig. 3 shows the empirical cumulative distribu-

tion function of P-waves differences energy for pa-

tients belonging to the control group. All the func-

tions exhibit similar behavior, with a high probability

of having low differences among different P-waves.

Conversely, Fig. 4 and Fig. 5 show the empirical cu-

mulative distribution function of P-waves differences

energy for AF patients. In this case the probability

of signiﬁcant differences among P-waves increases.

This effect is considerably more evident in the case of

AF relapse group. It is worth noting that the distri-

bution functions of AF relapse group are signiﬁcantly

different from the corresponding functions pertaining

to the control group. Moreover, considering the AF

relapse and the no-AF relapse groups it is possible

to appreciate differences in the distribution functions

that allows same level of discrimination between the

two groups.

The joint analysis of sample mean and sample

variance conﬁrms the difference between the control

group and the AF patients groups. Fig. 6 shows the

log-log plot of the pair sample mean and sample vari-

ance, namely (µ, σ

), for each patient. We can distin-

guish two regions: (i) low values region and (ii) high

0 0.1 0.2 0.3 0.4

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Differences energy

Probability

Figure 3: Controls: empirical cumulative distribution func-

tion of P-waves differences energy.

0 0.1 0.2 0.3 0.4

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Differences energy

Probability

Figure 4: AF patients (no-AF relapse group): empirical

cumulative distribution function of P-waves differences en-

ergy.

0 0.1 0.2 0.3 0.4

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Differences energy

Probability

Figure 5: AF patients (AF relapse group): empirical cumu-

lative distribution function of P-waves differences energy.

values region. In the low values region both mean

and variance of the differences energy is small. This

region identiﬁes controls and, hence, patients with-

out AF problems. Conversely, the high values re-

gion identiﬁes AF patients belonging to the AF re-

lapse group. In this ﬁgure, control group results to

be well separated from AF-relapse group. No-AF

relapse group turns out to be characterized by inter-

mediate values with respect to other groups. The

MEASURING P-WAVE MORPHOLOGICAL VARIABILITY FOR AF-PRONE PATIENTS IDENTIFICATION

483

Figure 6: Sample mean and sample variance of differences

energy for AF group and control group.

proposed method seems able to discriminate between

healthy and AF patients. In this regard it could be of

help in identifying AF-prone patients.

4 CONCLUSIONS

The availability of methods for measuring P-waves

variability over time represents a useful tool to deeply

understand the mechanisms underlying the atrial elec-

trical substrate, and may help in identifying patients

with substrates predisposing to AF. Indeed, the P-

wave variability is related to the dispersion of atrial

refractory period. In this paper, we propose a method

to measure such variability. It is based on the compu-

tation of the empirical cumulative distribution func-

tion of the differences energy among normalized P-

waves. The proposed method is able to discriminate

between AF patients and control subjects. This fact is

highlighted by the joint analysis of estimated statisti-

cal parameters such as the sample mean and the sam-

ple variance of differences energy. It is worth noting

that the proposed method exhibits some ability even

in discriminating between patients who experienced

AF relapse from patients who did not. In conclusion,

the analysis of the empirical distribution function of

differences energy among normalized P-waves seems

promising for capturing atrial anomalies and identify-

ing patients prone to AF.

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