**1. Introduction**

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Currently, atrial fibrillation (AF) guidelines are intended to assist physicians in clinical decision making by describing a range of generally acceptable approaches for the diagnosis and management of AF. However, these guidelines provide no recommendations that takes into account other aspects of the arrhythmia related with its computational analysis. For example, the proper application of spectral analysis, how to quantify different AF patterns in terms of organization, or how to deal with ventricular contamination before AF analysis are some aspects that could provide an improved scenario to the physician in the search of useful clinical information [1].

Both in surface and invasive recordings of AF the presence of ventricular activity has to be considered as a contaminant signal which has to be removed. In this respect, the proper analysis and characterization of AF from ECG recordings requires the extraction or cancellation of the signal components associated to ventricular activity, that is, the QRS complex and the T wave. Unfortunately, a number of facts hinder this operation [2]. Firstly, the atrial activity presents in the ECG much lower amplitude, in some cases well under the noise level, than its ventricular counterpart. Additionally, both phenomena possess spectral distributions notably overlapped, rendering linear filtering solutions unsuccessful. Within this context, several methods have been proposed to deal with this problem during last years. They go from a simple average beat subtraction [3], to the most advanced adaptive methods based on multidimensional signal processing [4] that will be detailed Section § 2.

From a clinical point of view, the estimation of the dominant atrial frequency (DAF), i.e., the repetition rate of the fibrillatory waves, is an important goal in the analysis of ECG recordings in AF. By comparing endocardial electrograms with ECGs, it has been established that the ECG-based AF frequency estimate can be used as an index of the atrial cycle length [5].

Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Joaquín Rieta and Alcaraz; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

©2012 Rieta and Alcaraz, licensee InTech. This is an open access chapter distributed under the terms of the

AF recordings with low DAF are more likely to terminate spontaneously and to respond better to antiarrhythmic drugs or cardioversion, whereas high DAF is more often associated with persistence to therapy [6]. The likelihood of successful pharmacological cardioversion is higher when the DAF is below 6 Hz [7]. Moreover, the risk of early AF recurrence is higher for patients with higher DAF [8] and, therefore, the DAF may be taken into consideration when selecting candidates for cardioversion. Section § 3 will provide to the reader basic concepts and recent advances in DAF estimation, as well as more elaborated techniques like time-frequency analysis or spectral modeling.

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Applications of Signal Analysis to Atrial Fibrillation

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**Figure 1.** Relevant time instants used by the ASVC algorithm. The points *si* and *ei* are the start and end points of the *i*-th QRST complex which is represented by **x***i*, respectively. The points *osi* and *oei* define the zones, at the beginning and the end of the

the average beat of the analyzed lead in order to optimize cancellation [16]. Other authors have proposed the idea of processing separately the QRS complex and the T wave [17]. This is because the depolarization waveform changes notably as a function of the heart rate, whereas

Finally, the most recently ABS method is based on adaptive singular value cancellation (ASVC) of the ventricular activity [13]. Given that the ECG signal presents a high degree of temporal redundancy which could be exploited for ventricular activity cancellation, the ASVC method detected all the R waves making use of the Pan and Tompkins technique [14]. Next, the starting and ending points of each QRS complex were detected and the complexes were aligned using their R peak timing. Figure 1 depicts the fiducial points and relevant time instants described herein. Once all the beats were temporally aligned, their eigenvector sequence was obtained by singular value decomposition (SVD). In this way, the highest variance provided the eigenvector considered as the representative ventricular activity [13]. Thereby, this activity was used as the primary cancellation template. Next the template was adapted to each QRST width and height and was temporally aligned with each R peak in the ECG. Finally, the customized template for each beat was subtracted from every QRST complex and the atrial activity estimation inside the complex was obtained. This SVD–based method provided a more accurate ventricular activity representation adapted to each individual beat and, as a consequence, a higher quality AA extraction in a wide variety

As an illustration on how the ABS-based methods can behave, Figure 2 plots the comparison between the simple ABS method introduced in [3] and the ASVC method presented in [13]. As can be observed, ventricular residua use to be present in the extracted AA, specially for the simple ABS method in (c). In fact, this is the main reason justifying the permanent

optimization of atrial activity extraction methods during last years.

*i*-th QRST complex, that will be processed to avoid sudden transitions after ventricular cancellation [13].

the repolarization waveform remains almost unchanged.

of AF recordings [13].

On the other hand, organization deals with strategies to quantify the repetitiveness of the AF signal pattern, thus providing very useful clinical information on the arrhythmia state. This relevant concept will be addressed in Section § 4, where the most important methods will be described [9, 10]. AF organization has demonstrated its clinical usefulness because indices of organization have been related to the electrophysiological mechanisms sustaining AF, or may be useful in the evaluation of strategies for AF treatment, such as catheter ablation or electrical cardioversion [11].
