# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 1 Introduction  
Decision making in healthcare domains is among the most critical tasks that a physician has to formulate. To check the health status of patients and/or health subjects, several qualitative and quantitative indicators are thoroughly analyzed, and a decision is then rendered. Medical decision-making could be seen as a classification problem. For instance, a specific disease can be recognized based on the combination of blood analysis, vitals characterization, and symptoms. In addition, one may wish to stratify patients based on medical imaging techniques (e.g., radiology, histopathology, etc.). Even if these tasks are common in healthcare practice, it is well-admitted that they are time-consuming and entail tedious and repetitive aspects mainly if the clinical data being measured are voluminous. Not to mention, the limited available places (as shown in COVID-19 pandemic) and number of healthcare professionals are other stressors complicating the hospital services. Therefore, it is thought that the automation of the medical decision-making process is important, as far as possible, especially for diseases with a major concern in public health, namely, cardiovascular diseases. Cardiovascular diseases are the first cause of death and they are projected to remain so [1; 2]. They represent a large share in which healthcare professionals are called upon to make decisions. Atrial fibrillation (AF), considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality, is spurring increased attention. More than 6 million people in Europe and 3 million people in the US are affected by AF [3; 4; 5]. The correct prevalence of AF is still unknown because AF is asymptomatic in many patients and thus remains undiagnosed [6]. In China, for instance, around 10 million people have AF [7], but the approximate prevalence of AF is 1% in the general population, which increases with age, more so in women than in men [8; 9; 10; 11]. Since the current population ageing trends and the improvement of survival rates from conditions predisposing to AF, the prevalence of AF is notably increasing [12; 13]. Automatic AF detection is strongly advisable, mainly due to the lack of symptoms raising the attention of either people undergoing AF or healthcare professionals. This makes AF a silent killer. However, AF events may unfold quickly, in short time periods, intrinsically hard to predict. This makes electrocardiography (Holter-ECG) recordings a strong requirement for a successful detection. The analysis of ECG and Holter-ECG recordings currently remains the gold-standard technique to diagnose AF. In fact, the two signature features of AF are typically RR interval irregularities and missing P-waves (replaced by rapid oscillations or fibrillatory waves), or combinations of both properties. In this sense, the development of automated classification systems has been, and continues to be, a dynamic research field, especially with the vast increase in processing speed and the evolution of data analysis science. Two approaches are emerging as promising means in the attempt to efficiently address automatic AF detection: univariate analysis in which data are analyzed according to a single variable, and in contrast, multivariate analysis where relationships between several variables are investigated. Comparative studies of classifiers dealing with AF detection have been already published in the literature. For instance, in 2003, [PERSON] et al. [14] reviewed methods and algorithms for the prediction of AF by studying the surface of electrocardiographic records in post-cardiosurgery or paroxysmal patients. [PERSON] et al. [15] compared nine AF detection algorithms under several criteria such as algorithm time resolution test, initial signal length, noise test, and computation time. This paper will focus on the employment of automated classification systems in AF detection.