**3. Nonlinear time series analysis**

2 Atrial Fibrillation

and persistent AF episodes and among patients with different organization degree, classified by following Wells' criteria [8], have been statistically detected. Moreover, patients successfully cardioverted making use of anti-arrhythmic drugs or ECV have been appropriately identified. Finally, with regard to the VA analysis, ventricular response has been widely characterized by quantifying nonlinear dynamics in interval series between successive R peaks, i.e., RR-interval series [9]. In this respect, multiple measures of fractal fluctuations, irregularity and geometric structure of time series have shown ability to evaluate the cardiovascular autonomic regulation before, during and after AF onset and characterize

Prior to the application of nonlinear indices to surface ECG recordings and intraatrial EGMs,

The surface ECG recording provides a widely used and non-invasive way to study AF. Some advantages of using the ECG include the ability to record data for a long period of time and the minimal costs and risks involved for the patient, in comparison with invasive procedures [10]. However, because of ECG represents the heart's electrical activity recorded on the thorax's surface, the signal is corrupted by different types of noise, which are picked up by the volume conductor constituting the human body. Thereby, in order to improve later analysis, these recordings need to be preprocessed. Filtering operations have been typically applied to the ECG for the reduction of noise sources, like baseline wandering, high frequency noise and powerline interference [11]. Thus, baseline wander is often removed making use of high-pass filtering (0.5 Hz cut-off frequency), high frequency noise with a low-pass filtering (70 Hz cut-off frequency) and powerline interference with an adaptive

Additionally, the *f* wave analysis from surface ECG recordings is complicated by the simultaneous presence of VA, which is of much higher amplitude. Thereby, the dissociation of atrial and ventricular components is mandatory [12]. Nowadays, several methods to extract the AA signal from surface ECG recordings exist. The most powerful techniques are those that exploit the spatial diversity of the multilead ECG, such as the method that solves the blind source separation problem [3] or the spatiotemporal QRST cancellation strategy [13]. However, the performance of these techniques is seriously reduced when recordings are obtained from Holter systems for paroxysmal AF analysis. The reason is that, generally, Holter systems use no more than two or three leads, which are not enough to exploit the ECG spatial information. For single-lead applications, the most widely used alternative to extract the AA is the averaged beat subtraction (ABS). This method relies on the assumption that the average beat can represent, approximately, each individual beat [12].

Nowadays, a variety of intraatrial recording modalities exists, such as bipolar and unipolar recordings from endocardial and epicardial electrodes, optical mapping and noncontact

Recently, a variety of extensions for this method have been proposed [12, 14].

they requires at least the basic preprocessing described in the following subsections.

the main electrophysiological characteristics of the atrioventricular (AV) node.

**2. Preprocessing of cardiac recordings**

**2.1. Surface ECG recording**

notch filtering.

**2.2. Intraatrial EGM**
