Automated Atrial Fibrillation Detection by ECG Signal Processing: A Review

[EN] Cardiovascular diseases are the main cause of death in the world, according to the World Health Organization. Among them, ischemic heart disease is at the top, followed by a stroke. Several studies have revealed that atrial fibrillation (AF), which is the most common cardiac arrhythmia, increases up to five fold the overall risk of stroke. As AF can be asymptomatic, approximately 20% of the AF cases remain undiagnosed. AF can be detected by analyzing electrocardiography records. Many studies have been conducted to develop automatic methods for AF detection. This paper reviews some of the most relevant methods, classified into three groups: analysis of heart rate variability, analysis of the atrial activity, and hybrid methods. Their benefits and limitations are analyzed and compared, and our beliefs about where AF automatic detection research could be addressed are presented to improve its effectiveness and performanceThe authors report no conflicts of interest. The authors alone are responsible for the content and writing of this paper. This research is partially supported by Universidad Tecnologica de Bolívar, Cartagena Colombia, (Grant No. C2018P022) and by Erasmus+ KA107 (ICM). This research did not receive any funding from NIH. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submittedGiraldo-Guzmán, J.; Contreras-Ortiz, SH.; Kotas, M.; Castells, F.; Moron, T. (2021). Automated Atrial Fibrillation Detection by ECG Signal Processing: A Review. Critical Reviews in Biomedical Engineering. 49(3):31-50. https://doi.org/10.1615/CritRevBiomedEng.2022041650315049