PRINCIPAL COMPONENT ANALYSIS OF THE P-WAVE

Quantification of Not-Dipolar Components of Atrial Depolarization

Federica Censi, Giovanni Calcagnini, Pietro Bartolini

Department of Technologies and Health, Istituto Superiore di Sanità, Roma, Italy

Chiara Ricci

Department of Statistics, Probability and Applied Statistics, Univ. of Rome "La Sapienza", Rome, Italy

Renato Pietro Ricci, Massimo Santini

Department of Cardiology, San Filippo Neri Hospital, Roma, Italy

Keywords: Principal component analysis, atrial fibrillation, P-wave, not dipolar components.

Abstract: Aim of this study is to perform the principal component analysis (PCA) of the P-wave in patients prone to

atrial fibrillation (AF). Eighteen patients affected by paroxysmal AF and implanted with dual chamber

pacemakers were studied. Two 5-minute ECG recordings were performed: during spontaneous (SR) and

paced rhythm (PR). ECG signals were acquired using a 32-lead mapping system (2048 Hz, 24 bit, 0-400 Hz

bandwidth). For each patient, PCA of the averaged P-waves extracted in any of the 32 leads has been

performed. We extracted PCA parameters related to the dipolar (using the first 3 PCs) and not dipolar (from

the 4

to the 32

PCs) components of the P-wave. The number of PCs according to the latent root criterion

ranges between 2 and 3 during SR and between 2 and 4 during PR. PCA parameters related to the 3 largest

PCs, and describing the dipolar component of the P-wave, did not significantly differ during SR and PR.

The not dipolar components during SR were significantly lower than during PR (PCAres%: 0.03±0.06 vs

0.12±0.21, p=0.001; PCAres [mV

]: 0.10±0.14 vs 0.49±0.73, p=0.001). Factor analysis showed that on

average all leads contributes to the first principal component

1 INTRODUCTION

Atrial fibrillation (AF) is the most commonly

encountered arrhythmia in clinical practice. It is

defined by the absence of coordinated atrial systole,

since it results from multiple reentrant electrical

wavelets that move randomly around the atria.

Althought it is not a lethal disease, AF may increase

mortality up to 2-fold, primarily due to embolic

stroke.

Indeed, the lack of coordinated atrial contraction

leads to unusual fluid flow states through the atrium

that could favour the formation of thrombus at risk

to embolize, expecially after return to normal sinus

rhythm.

The incidence of atrial fibrillation increases

significantly with advancing age. When a patient

spontaneously alternates between AF and a normal

rhythm, the condition is known as paroxysmal AF.

When a patient continues with AF as the dominant

cardiac rhythm without reversion to the normal

rhythm, the condition is known as chronic AF. Two

main electrophysiological conditions are indicated

for AF initiation and perpetuation (Clavier et al.,

2002): slower conduction velocity in some atrial

areas and heterogeneity of cell refractory periods.

This heterogeneity of structural and

electrophysiologcal properties leads to a longer and

more fragmented P-wave (Davies et al., 1963;

Kawano et al., 1988; Dilaveris et al., 1998).

Thus, many studies focused on the analysis of

the P-wave to extract parameters to recognize a

patient with paroxymal AF as well as to predict the

development of AF (Dilaveris et al., 1998; Jordaens

et al., 1998; Dilaveris et al., 2001; Darbar et al.,

2002; Dilaveris et al., 2002).

Given the technical difficulties to analyze the P-

wave, and the different acquisition and processing

551

Censi F., Calcagnini G., Bartolini P., Ricci C., Pietro Ricci R. and Santini M. (2008).

PRINCIPAL COMPONENT ANALYSIS OF THE P-WAVE - Quantiﬁcation of Not-Dipolar Components of Atrial Depolarization.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 551-557

DOI: 10.5220/0001060005510557

 SciTePress

Figure 1: Scheme of the electrodes positioning and of the P-wave pre-processing procedure.

systems used, these studies often lead diverse

and not-comparable results in terms of cutoff values.

Indeed, the analysis of the T-wave, corresponding to

the ventricular repolarization, has been extensively

used to quantify repolarization inhomogeneity that

may create an arrhythmogenic ventricular substrate.

Promising results have been obtained by measuring

the QT interval (QT dispersion) and by performing

the principal component analysis of the T-wave (De

Ambroggi et al., 1997; Acar et al, 1999; Malik et al.,

2000; Kesek et al., 2004).

The former analysis have been already applied to

the P-wave: P-wave dispersion (which is the

difference between the maximum and the minimum

P-wave duration recorded from the 12 standard

leads), has been shown to distinguish patients with

paroxymal AF (Dilaveris et al., 1998; Jordaens et al.,

1998; Dilaveris et al., 2001; Darbar et al., 2002;

Dilaveris et al., 2002).

PCA of the T-wave has been extensively used to

quantify both the complexity and the not dipolar

components of the T-wave (De Ambroggi et al.,

1997; Acar et al, 1999; Malik et al., 2000; Kesek et

al., 2004): particularly, if the ECG would be

completely explained by a single electrical dipole,

the three largest principal components (PCs), and

their corresponding orthogonal eigenvectors, would

span the real three dimensional space (dipolar

components), while the remaining PCs (not dipolar

components) would be zero (Kesek et al., 2004).

For the T-wave it has been demonstrated that the

not dipolar components, quantified by the PCA, are

not zero, and reflect local repolarization

inhomogeneity (Kesek et al., 2004). PCA has never

been applied to the P-wave.

Following the approach already used for the

analysis of the T-wave, the aim of this study is to

perform the PCA of the P-wave in patients prone to

AF in order to: 1) evaluate how many principal

components are necessary for an AF patient and in

which way they are correlated with the ECG leads;

2) evaluate if and to which extent pacing affects the

dipolar and the not dipolar components of the atrial

depolarization (as quantified by PCA).

2 METHODS AND MATERIALS

2.1 Study Population

Nineteen patients with paroxysmal atrial fibrillation

and permanent dual chamber pacemakers (AT500-

Medtronic Inc., Minneapolis, MN, USA) were

recruited from S. Filippo Neri Hospital, Rome, Italy.

The AT500 device combines atrial sensing and

detection algorithms for monitoring and diagnostics,

and atrial therapy delivery functions.

The system can store up to 35 episodes of atrial

tachycardia/ flutter with electrograms and up to 128

episodes text summaries, without electrograms.

This pacemaker allows for accurate classification

of atrial fibrillation episodes, with detailed

information about episode instant of occurrence and

duration, and further features three distinct

programmable pacing algorithms that suppress atrial

tachyarrhythmia trigger mechanisms.

When an episode occurs, the device is also

programmed for arrhythmia termination. Three atrial

pace-termination algorithms can recognise treatable

atrial tachycardias and deliver antitachycardia pace-

therapies to restore sinus rhythm.

The study population consisted of 9 female and 10

men, aged 72±10.

P-wave

averaging

P-wave

template

P-wave

extraction

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

552

2.2 Experimental Protocols

Two five-minute recordings were performed for

each subject. In the first recording the pacemaker

was programmed in VVI mode, i.e. in single-

chamber ventricular pacing mode set to a rate of

40/min, so that to have spontaneous rhythm.

In the second recording, pacemaker settings were

changed back to the common operating DDD mode,

i.e. with both atrial and ventricular pacing functions

activated.

Recordings were made using a multi-lead

mapping system for high-resolution biopotential

measurement (ActiveTwo, Biosemi, The

Netherlands).

The system is made of a battery powered isolated

AD box that digitises the signals and transfers them

to a PCI receiver on computer through a fibre-optic

connection. The signals were digitised at a sampling

rate of 2048 Hz and a resolution of 24 bits with a

frequency response in the full DC-400Hz range.

No further filtering was applied to the data.

Thirty-two leads were positioned on the thorax

(figure 1), to allow accurate recordings of atrial

signals.

ECG recordings were acquired as single-ended

signals, with respect to a common reference

position. Before starting the acquisition, signals

were visualised on a computer screen to check for

good electrode contact.

2.3 P-wave Pre-processing

Every lead signal was pre-processed and analysed to

extract the average P-wave characteristic.

The first step is to isolate the P-waves from the

acquired signals: after detecting the R-wave (using

an algorithm similar to that proposed by Pan and

Tompkins) (Pan and Tompkins, 1985), P-waves are

extracted in a 200ms-long window (410 samples)

starting 300ms before the R-wave (figure 1).

Secondly, a beat-by-beat linear piecewise

interpolation was used to remove baseline wander,

on each P-wave. Fiducial points for linear

interpolation were taken from TP and PQ tracks of

each beat.

Third, a P-wave template is constructed (figure

1) by averaging each extracted P-wave having a

cross-correlation coefficient with the current

template higher than 0.9.

In order to take into account the variations in PR

interval and/or the inaccuracy in R-wave detection

before averaging P-waves were aligned according to

the lag at which the cross-correlation function

between the current averaged P-wave and each

single P-wave shows its maximum (coherent

averaging procedure).

The coherent averaging procedure went on until

200 beats were included. If the residual noise level

(measured in the isoelectric TP track) remained at

more than 1μV even after averaging of 200 beats,

averaging procedure continued until the noise level

reached a value lower than 1μV.

If it was impossible, the lead was excluded from the

study.

2.4 Principal Component

Analysis – Measures of Atrial

Depolarizatrion

Characteristics

For each patient, PCA of the 32 averaged P-waves

extracted from the 32 leads has been performed.

Since PCA transforms the measured P-wave to

virtual parameters that are mutually independent

(orthogonal), the 3 largest PCs would contain all the

information in the P-wave stemming from the

vectorial concept of a single electrical dipole.

Following an approach already applied to the T-

wave (Kesek et al., 2004; Acar et al, 1999), the other

principal components (in this case from the 4

to the

) represent the not dipolar components of the

atrial depolarization.

We thus extracted the following parameters:

where s

denotes the i-th eingenvalue associated with

the i-th principal component. We also extracted the

number of principal components suggested by the

latent root criterion (PCA number).

In addition, in order to estimate to which extent

each lead contributes to the first principal

component, factor loadings have been calculated.

Analogous to Pearson's coefficient, the squared

PRINCIPAL COMPONENT ANALYSIS OF THE P-WAVE - Quantification of Not-Dipolar Components of Atrial

Depolarization

553

Table 1: Results of the PCA parameters during spontaneous rhythm (SR) and during paced rhythm (PR) for all 18 patients.

Figure 2: Example of the 32 P-wave templates and of the results of the PCA for one patient.

[mV

] [%]

[%] [%]

[%]

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

554

factor loading is the percent of variance in that

variable explained by that PC (i.e the degree of

correlation between the original data and the first

principal component expressed in percentage).

In addition, in order to estimate to which extent

each lead contributes to the first principal

component, factor loadings have been calculated.

Analogous to Pearson's coefficient, the squared

factor loading is the percent of variance in that

variable explained by that PC (i.e the degree of

correlation between the original data and the first

principal component expressed in percentage).

3 RESULTS

Figure 2 shows the 32 P-wave templates and the

results of the PCA for one patient.

Table 1 summarizes the results obtained by the

PCA parameters. The number of principal

components according to the latent root criterion

ranges between 2 and 3 (2,56±0,51) during

spontaneous rhythm and between 2 and 4 during

pacing (2,67±0,69, p=0,6).

PCA parameters related to the three largest PC

(PCA

[%], PCA

[%] and PCA

[%]), that describe

the dipolar component of the P-wave, did not

significantly differ during spontaneous and paced

rhythm (table 1, Wilcoxon test for paired data). The

not dipolar component (figure 3) as defined by both

PCAres and PCAres% during spontaneous rhythm

were significantly lower than during pacing

(PCAres%: 0,03±0,06 vs 0,12±0,21, p=0,001;

PCAres[mV

]: 0,10±0,14 vs 0,49±0,73, p=0,001).

Figure 3: Notdipolar components as defined by both

PCAres and PCAres% during spontaneous rhythm and

during pacing.

Factor analysis showed that on average all leads

contributes to the first principal components. Figure

4 shows the factor loadings averaged (in absolute

values) all over the population during spontaneous

rhythm and during pacing. Each lead but one in

spontaneous rhythm (lead A17) correlates with the

first principal component.

4 DISCUSSION

Analysis of the P-wave had been extensively

developed to extract parameters related to atrial

depolarization heterogeneities useful to recognize

patients with paroxymal AF or to predict the

development and the perpetuation of AF (Dilaveris

et al., 1998; Jordaens et al., 1998; Dilaveris et al.,

2001; darbar et al., 2002; Dilaveris et al., 2002).

However, the technical difficulties to acquire and

process the P-wave, had so far limited its clinical

use. Indeed, promising results have been obtained by

performing the PCA of the T-wave, in terms of

quantification of ventricular repolarization

inhomogeneity that may create an arrhythmogenic

ventricular substrate (De Ambroggi et al., 1997;

Acar et al, 1999; Malik et al., 2000; Kesek et al.,

2004).

We hereby used an 32-lead ECG acquisition

system particularly suitable for P-wave analysis,

having 24 bit resolution and being DC-coupled. We

performed the PCA of the P-wave in patients prone

to AF. PCA has been applied to the average P-wave

extracted in any of the 32 leads.

For each patient we extracted the same PCA

parameters employed for the T-wave (Kesek et al.,

2004; Acar et al, 1999). As for the T-wave, the PCA

parameters related to the first three PCs are

associated to the dipolar component of the P-wave,

while the remaining PCs (form the 4

to the 32

)

are associated with the not dipolar component of the

P-wave.

To our knowledge this is the first time the PCA

is performed on the P-wave, thus physiological

interpretation and critical discussion can be related

only to previous experimental evidences of

ventricular conduction disturbance (PCA of the T-

wave ) (Kesek et al., 2004; Acar et al, 1999).

The first important result is that pacing provokes

a significant increase of the not dipolar components

of the P-wave. Thus atrial pacing changes the atrial

activation, disturbing the normal atrial

depolarization process and generating additional

paths not explainable by a single P-vector. Such a

PRINCIPAL COMPONENT ANALYSIS OF THE P-WAVE - Quantification of Not-Dipolar Components of Atrial

Depolarization

555

Figure 4: Factor loadings obtained during spontaneous rhythm and during pacing.

result is in agreement with previous studies showing

an increase in P-wave duration and low-frequency

energy during pacing respect to sinus rhythm in

patients with AF (Keane et al., 1995). Non-dipolar

components is plausible to be associated with local

atrial depolarization inhomogeneity: pacing seems to

provoke parts of the myocardium depolarized in a

normal sequence and parts depolarized from an

abnormal direction.

The second important results is that, on average,

all the 32 leads contributes to the first PC, having a

significant correlation coefficient with almost all

variables.

Since any leads systematically show a not

significant correlation with first PC, each lead seems

to contribute to a similar extent to the dipolar

component. However, we found an inter-patient

variability for the factor loadings – some patients

had not significant factor loadings in some leads.

This result suggests that maps of the correlation with

the first PC (or of the average correlation with the

first 3 PCs) could help in identifying those leads (i.e.

body surface zones) which mainly contribute to the

dipolar component of the atrial depolarization.

In conclusion, the study of the dipolar and not

dipolar components of the P-wave could provide

important information not present in a classical

ECG. If the assumption that the not dipolar signal is

associated with local depolarization inhomogeneity

of the atrium is correct, the PCA is a useful

mathematic tool to deeply investigate the atrial

conduction disturbances as well as the effects of

pharmacological or electrical therapies. This first

study tempting the PCA on the P-wave shows that

pacing alters the atrial depolarization patterns,

provoking an increase of the not dipolar component

of the P-wave.

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