Physiologically Motivated Detection of Atrial Fibrillation

Atrial Fibrillation (AF) is the most common arrhythmia and it is estimated to affect 33.5 million people worldwide. AF is associated with an increased risk of mortality and morbidity, such as heart failure and stroke and affects mostly older persons and persons with other conditions (e.g. heart failure and coronary artery disease). In order to prevent such life threatening and life quality reducing conditions it is essential to provide better algorithms, capable of being integrated in low-cost personalized health systems. This paper presents a new algorithm for AF detection, which is based on the analysis of the three physiological characteristics of AF: 1) Irregularity of heart rate and; 2) Absence of P-waves and 3) Presence of fibrillatory waves. Based on these characteristics several features were extracted from 12-lead electrocardiograms (ECG) and selected according to their discrimination ability. The classification between AF and non-AF episodes was performed using a Support Vector Machine (SVM) classification model. Our results show that the identification of the fibrillatory patterns, using the proposed features, extracted from the analysis of 12-lead ECG improves the performance of the algorithm to a sensitivity of 88.5% and specificity 92.9%, when compared to our previous single-channel approach, in the same database.