**4. Spatial analysis of the P-wave**

Finally, it has to be remarked that other attempts to quantify the P-wave morphology can also be found in the literature. To this respect, a classification rate higher than 90% among P-waves with different morphologies has been reached by means of systems identification in [64]. On the other hand, the AF risk after post-coronary artery bypass grafting has been successfully quantified by analyzing the singular value decomposition of the P-wave morphology [60]. Similarly, Clavier et al. [24] have presented an automatic method based on a hidden Markov model and wavelet transform to detect and delineate the P-wave. Moreover, they also proposed a polynomial characterization of this wave, thus noticing the need of higher orders to represent slight morphological details of the P-wave. Finally, the P-wave energy has also been analyzed by several authors from wavelet and frequency domains [16, 17], providing P-

**Figure 11.** Typical P-wave width *W* variability over time from representative ECG intervals from (a) a healthy subject,

number of 10 P-wave-length intervals number of 10 P-wave-length intervals number of 10 P-wave-length intervals

waves with higher energy when the risk of AF development was increased.

0 0.02 0.04 0.06 0.08 0.10 0.12

0 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16

> healthy subjects

gaussian modeling of the P-wave for healthy subjects, patients far from PAF, and patients close to PAF.

(a) α = 0.0011 (b) α = 0.014 (c) α = 0.046

patients far from PAF

**Figure 10.** Boxplots showing the distribution of the fitting line slope associated to each analyzed parameter from the

α for Pdur

patients close to PAF

0 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16

> 0 0.2 0.4 0.6 0.8 1 1.2 1.3 1.6

> > healthy subjects

patients far from PAF

patients close to PAF

α for PPk

0 50 100 150 200 250 300 350 400 450 50 100 150 200 250 300 350 400 450 500

0

patients close to PAF

α for

*W*

α for *C*

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 x 10-4

44 Abnormal Heart Rhythms

0 0.2 0.4 0.6 0.8 1.0 x 10-3

*W* variability (ms)

healthy subjects patients far from PAF

<sup>0</sup> <sup>50</sup> <sup>100</sup> <sup>150</sup> <sup>200</sup> <sup>250</sup> <sup>300</sup> <sup>350</sup> <sup>400</sup> <sup>450</sup> <sup>500</sup> <sup>0</sup>

(b) patients far from PAF, and (c) patients close to PAF.

α for ε

α for *A*

> The P-wave duration has also been widely characterized from the standard 12-lead ECG recording. Thus, the P-wave duration has been normally measured in at least ten of the 12 surface leads, with the maximum being the longest P-wave duration in any lead and the minimum being the shortest P-wave duration in any lead [38]. Moreover, the P-wave disper‐ sion has been defined as the difference between the maximum and the minimum P-wave duration [39]. Whereas the minimum P-wave duration has only provided recently significant clinical information to identify an increased risk of lone AF [65], the maximum one and its dispersion have shown to be widely useful in the prediction of PAF. To this respect, Dilaveris et al. [38] found a high correlation between the maximum P-wave duration and the risk of idiopathic PAF. Moreover, the P-wave dispersion complemented this metric for a clearer separation between patients with PAF and normal controls. These findings were validated two years later by Aytemir et al. [47], who analyzed a slightly wider database. Additionally, De Bacquer et al. [66] showed that the maximum P-wave duration was a very important risk indicator for the development of AF over 10 years. In agreement with these works, Andriko‐ poulos et al. [67] showed that the maximum P-wave duration and its dispersion were signifi‐ cantly higher in patients with PAF than in control subjects. However, these authors also reported that the variance of the P-wave duration in the 12-lead ECG was a stronger predictor of the onset of idiopathic PAF than the previous metrics. The same result was also obtained by Perez et al. [40], who measured the standard deviation of the P-wave in the ECG. However, in this case, more than 40,000 patients who were followed for the development of AF for at least 5 years were analyzed. In view of this outcome, the authors suggested that the P-wave variance may account for the differences in atrial conduction across different areas, thus reflecting more accurately the heterogeneity of the diseased atria.

> In addition to these works, others have also corroborated the usefulness of the P-wave dispersion to predict the onset of PAF. Thus, Dilaveris et al. [68] showed the ability of this metric to predict frequent symptomatic AF paroxysms. In the same study, a significant positive

correlation was observed between the P-wave dispersion and its maximum duration. Simi‐ larly, a relevant negative correlation was noticed between the P-wave dispersion and its minimum duration. In another publication, the P-wave dispersion also showed to be a sensitive and specific ECG predictor of paroxysmal lone AF. Furthermore, it also provided a significant correlation with the maximum P-wave duration and a weak, although significant, association with age [47]. On the other hand, Dilaveris et al. [69] and Ciaroni et al. [70] revealed the Pwave dispersion as an independent predictor of the onset of PAF in the hypertensive popula‐ tion. Indeed, this metric was found to be significantly higher in hypertensives with a history of PAF than those without history of arrhythmia. Finally, it is interesting to mention that Koide et al. [71] concluded that the P-wave dispersion was a clinically useful predictor of progression from paroxysmal to persistent AF. In this study, more than 200 patients with a diagnosis of PAF were followed for more than 60 months.
