**2. Analysis of atrial premature complexes**

**1. Introduction**

28 Abnormal Heart Rhythms

Atrial fibrillation (AF) has been described as the arrhythmia of the twenty-first century given that it is the most commonly diagnosed sustained cardiac arrhythmia, affecting up to 1% of the general population and up to 15% of the population older than 70 years of age [1, 2]. Despite extensive research during the last decades, it is still unclear how AF starts and perpetuates itself [2]. Once AF appears, its usual evolution presents three different stages [3]. In the first one, named paroxysmal AF (PAF), patients present auto-limited episodes with a duration shorter than seven days, which terminate spontaneously without the need of medical inter‐ vention. However, approximately between 15 and 31% of PAF patients progress to permanent AF in four to eight years [4]. During this progression, recurrent episodes normally appear, but it is also still unknown why the duration of these episodes varies from patient to patient and from episode to episode [5]. Therefore, once a PAF episode has terminated spontaneously, the early prediction of the onset of the next PAF episode is a relevant clinical challenge. Such a prediction may minimize risks for AF patients and improve their quality of life, since a preventive therapy may be used to avoid recurrence of the arrhythmia. Thus, the maintenance of normal sinus rhythm may reduce symptoms, get better hemodynamics, and minimize the atrial remodeling, which increases the probability of recurrence of AF [6]. Moreover, the risk

The normal electrical conduction in the heart is generated by the sinoatrial node and is propagated throughout the right atrium and through the Bachmann's bundle to the left atrium, thus defining the P-wave in the electrocardiogram (ECG). However, when this progressive conduction is altered by accessory pathways, reentries, or conduction delays, the P-wave morphology will reflect this fact [7]. Currently, it is well known that both slowed conduction velocity as well as inhomogeneous cell refractory periods in several atrial regions are atrial electrophysiological alterations provoking and maintaining AF [8]. Such atrial conduction abnormalities result in prolonged and highly variable P-waves [9]. Thereby, the analysis of the

Within this context, the noninvasive P-wave analysis has been performed, following two main recording approaches. On the one hand, it has been based on the standard 12-lead ECG [11]. On the other hand, some authors have based their studies on the P-wave vector magnitude obtained from Frank's orthogonal leads [12]. However, in both alternatives, only averagedsignal P-waves have been widely analyzed in order to minimize the shortcomings derived from the relatively low amplitude of this wave in the ECG [13]. Indeed, the duration of the averaged P-wave has been widely analyzed, and its prolongation has been associated with history of AF, development of arrhythmia after bypass surgery, and progression from paroxysmal to persistent AF [9]. Additionally, the fragmentation of the P-wave template has also been broadly studied from the spectral, gaussian, and wavelet domains [14, 15, 16]. In this case, a wide variety of features associated with the P-wave morphology have provided the ability to discern between PAF patients and healthy subjects [15, 16], to quantify the effects of different antiarrhythmic drugs [17], and to stratify the risk of recurrence of AF after electrical cardioversion [14], catheter ablation [18, 19], and coronary artery bypass grafting [20].

of suffering thromboembolic events can also be notably reduced [2].

P-wave has gained increasing interest in the last years [10].

Several studies have shown that most PAF episodes are initiated by the presence of rapidly firing atrial ectopic foci [28, 29]. Given that their occurrence results in premature atrial depolarizations [30], the identification of a wide number of premature atrial complexes (PACs) in the ECG has proved to be a successful predictor of imminent PAF onset [31, 32]. To this respect, Thong et al. [31] defined an isolated PAC as those preceded and followed by two cycles of the prevalent rhythm. Moreover, they identified four separate categories of isolated PACs. First, Fig. 1(a) shows a PAC with sinus node reset. The sinus node is reset by the ectopic Pwave within normal conduction time, thus provoking that the next RR interval is within 100 ms of the prevalent one. Second, Fig. 1(b) shows an interpolated PAC. In this case, the sinus node is not reset and the corresponding QRS is located in a normal RR interval with no alteration of the prevalent rhythm. Third, Fig. 1(c) shows a PAC in which the sinus node reset has been delayed. A delay occurs in the path of electrical conduction to the sinus node, thus increasing the next RR interval by approximately 100 ms. Finally, Fig. 1(d) displays a PAC in which a full compensatory pause occurs. In this case, the PAC causes the AV junction to be refractory, thus doubling the RR interval compared with the prevalent one.

Making use of the database proposed by the Computers in Cardiology Challenge, which is freely available in PhysioNet [33], the authors compared the two provided ECG leads to detect rhythm changes. These rhythm changes were associated with the ECG intervals preceding the immediate onset of PAF, while those intervals more than 45 minutes far from a PAF episode were related to a normal rhythm. The authors counted the number of isolated PACs in categories 2–4 for each lead of every subject and followed the criteria presented in Fig. 2 to identify rhythm changes. Three or more consecutive PACs without any intervening long

**Figure 1.** ECGs of various types of PACs according to Thong et al. [31]. (a) PAC with sinus reset, *R*3*R*<sup>4</sup> =*R*1*R*2. (b) Interpolated PAC, *R*2*R*<sup>4</sup> =*R*1*R*2. (c) PAC with delayed sinus node reset, *R*3*R*<sup>4</sup> >*R*1*R*2. (d) PAC with full compensato‐ ry pause, *R*2*R*<sup>4</sup> =2*R*1*R*2. QRS of PACs are similar to other QRS. RR timing is used to differentiate the four types of PACs. Note inverted P-waves in (a), (c), and (d). Horizontal tick marks are 200 ms apart.

intervals were considered to meet the PAT criterion. Regarding the PAC test, if the difference in the number of PACs between the two leads of a subject was higher than or equal to two, a rhythm change was marked. Finally, to account for atrial bigeminies and trigeminies, changes in RR series higher than 70 ms were first detected. After, a 10-point boxcar filter was applied to the RR series and the averaged power of the filtered signal was computed. A change of rhythm was detected when the average power was higher than 0.95 and a difference between leads was higher than 1.5. Finally, the algorithm allowed to discern between ECG intervals far from PAF and close to PAF with an accuracy around 90%.

This result was greater than those achieved by other participants in the Computers in Cardi‐ ology Challenge, which proposed different algorithms to detect the presence of PACs. Thus, Recent Advances in the Noninvasive Study of Atrial Conduction Defects Preceding Atrial Fibrillation http://dx.doi.org/10.5772/60729 31

**Figure 2.** Flow diagram for the algorithm proposed by Thong et al. [31] to identify the onset of PAF.

intervals were considered to meet the PAT criterion. Regarding the PAC test, if the difference in the number of PACs between the two leads of a subject was higher than or equal to two, a rhythm change was marked. Finally, to account for atrial bigeminies and trigeminies, changes in RR series higher than 70 ms were first detected. After, a 10-point boxcar filter was applied to the RR series and the averaged power of the filtered signal was computed. A change of rhythm was detected when the average power was higher than 0.95 and a difference between leads was higher than 1.5. Finally, the algorithm allowed to discern between ECG intervals far

**Figure 1.** ECGs of various types of PACs according to Thong et al. [31]. (a) PAC with sinus reset, *R*3*R*<sup>4</sup> =*R*1*R*2. (b) Interpolated PAC, *R*2*R*<sup>4</sup> =*R*1*R*2. (c) PAC with delayed sinus node reset, *R*3*R*<sup>4</sup> >*R*1*R*2. (d) PAC with full compensato‐ ry pause, *R*2*R*<sup>4</sup> =2*R*1*R*2. QRS of PACs are similar to other QRS. RR timing is used to differentiate the four types of

This result was greater than those achieved by other participants in the Computers in Cardi‐ ology Challenge, which proposed different algorithms to detect the presence of PACs. Thus,

from PAF and close to PAF with an accuracy around 90%.

PACs. Note inverted P-waves in (a), (c), and (d). Horizontal tick marks are 200 ms apart.

30 Abnormal Heart Rhythms

Langley et al. [34] identified the potential ectopics from those provoking RR intervals shorter than 80% of their average. Next, those beats with an RR value exceeding ±10% of their mean were considered as atrial, whereas those altering more than ±30% of that value were considered as ventricular. In [35], a morphological comparison among potential ectopic and normal beat was also considered. Finally, ectopic beats were considered as those with a QRS morphology similar to normal beats. In a similar line, Schreier et al. [36] considered that those beats with a very different morphology compared to their neighbors could cause the onset of PAF. On the other hand, symbolic analysis was applied to the RR series to identify acceleration and decelerations in the heart rate and associate them with the onset of PAF. Finally, Hickey et al. [32] identified APCs by using an algorithm based on two steps. First, a beat was flagged as a suspected APC if the RR interval preceding it was 15% shorter than a defined local moving average of the surrounding RR intervals. In the second stage, the area, width, and amplitude of the QRS were computed. If all these parameters differed more than 10% from a normal beat, they were confirmed as APCs. The first 100 beats from a regular sinus rhythm were used to compute the parameters associated with normal beats. This algorithm is based on the fact that ventricular ectopics present morphologies very different from normal beats. Although the accuracy of this algorithm to identify ECG intervals immediately before the onset of PAF was slightly lower than those presented by Thong et al.'s algorithm, its combination with infor‐ mation obtained from the RR series spectral analysis yielded a better outcome (i.e., an accuracy around 98%) [32]. Indeed, subjects with imminent PAF showed to have highly correlated lowfrequency and high-frequency components in their heart rate. Thus, the authors suggested that sympathetic and parasympathetic autonomic activity may be coupled in these subjects [32].
