# The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals
## 1 Introduction  
Atrial fibrillation(AF) is the most common sustained cardiac arrhythmia in clinical practice. Patients are at increased risk for death, heart failure, hospitalization, and thrombo-bembolic events due to AF [1, 2, 3]. AF is a global health care problem with evidence suggesting increasing prevalence and incidence worldwide [4, 5, 6]. The prevalence of AF has almost doubled in the last decade, being currently 2% in unselected patient populations. Its occurrence varies with age (present in almost 20% of octogenarians) and concomitant diseases [7, 8]. Moreover, AF can increase the risk of strokes, heart failure and other heart-related complications and it indicates a high probability of coexisting cardiovascular or systemic diseases [9]. Many approaches have been employed to detect AF episodes based on electrocardiogram (ECG) signal analysis [10]. Nevertheless, the occurrence of this arrhythmia is intermittent (so-called paroxysmal AF (PAF)) and it is frequently misdiagnosed. However, it is necessary to improve PAF detection to enable appropriate strategies to be appropriately implemented to prevent or reduce the risk of stroke, the most severe consequence of AF. Ambulatory systems represent a suitable approach for continuous monitoring of cardiac patients outside the clinical environment. Ambulatory monitors are small, portable electrocardiograph machines. Each type of monitor has unique features related to limitation inlength of recording time and ability to send the recordings over wireless data networks to centralized servers. The ECG signal is normally measured on the body surface, so the signal acquisition is a non-invasive; this approach is widely used for heart disease diagnoses and healthcare [11; 12]. The compressibility of ECG data is based on the analysis of the important components of the signal; that is, P wave, QRS wave, T wave, and ST interval. AF can be characterized in an ECG signal by the absence of discrete P-waves which are replaced by rapid deflections of relatively low amplitude and of varying timing and morphology known as fibrillatory waves (f-waves), as well as unpredictable depolarisation of the ventricles (no pattern to R wave occurrence in the ECG). Fibrillation of the atria may result in a chaotic disturbance of the isoelectric line, however, this finding in an ECG can be misleading as muscular activity or other agitation can have a similar effect [13]. Equally, it can often be difficult to tell if P waves are present or not in an ECG. In recent years, there has been increasing interest in the development of computer-aided diagnostic approaches for many common chronic illnesses, particularly cardiovascular conditions such as AF. Several approaches have been developed for automated computer-based diagnosis of AF from ECG waveforms. [PERSON] et al. recently proposed a stationary wavelet transform and support vector machine combination to detect AF [14], while other approaches have employed P-wave absence, f-wave presence, and noise level to achieve substantial improvement in detecting brief AF episodes. Due to the difficulty of detecting atrial activity from surface ECG and the relative success of QRS detection algorithms, methods focusing on measures such as R-R interval sequences, R-R variation and density histograms of R-R intervals [15] are typically employed for AF detection. Ambulatory monitoring systems such as Holter monitors record ECG signals continuously, generally for a period of between 24 to 48 h. Given the sampling frequencies involved and the potentially long monitoring duration required, such an architecture involves the acquisition of large amounts of ECG data [16; 17; 18; 19; 20]. Coupled with this is the fact that truly ambulatory devices are of necessity battery-operated; as a result, one of the most important requirements for such ambulatory monitoring systems is low-energy operation.