Detection of Atrial Fibrillation from ECG using BTD Tensor Decomposition

Atrial fibrillation (AF) is a common cardiac arrhythmia associated with various cardiovascular diseases and has a significant impact on mortality around the world. This work focuses on the detection of AF using data collected from cardiac monitoring through the Electrocardiogram (ECG), proposing new attributes for the prediction of AF. In particular, the present work proposes novel convergence and optimization indicators derived from Block-term Decomposition (BTD), applied to five RRI ECG segments, combined with RRI intervals (RRI) to improve the detection of AF. These features were used with treebased machine learning algorithms to classify signals as Atrial Fibrillation (AF) or Normal Sinus Rhythm (NSR). The study also discusses data acquisition from three different ECG databases: Atrial Fibrillation Database (AFDB), Long-term Atrial Fibrillation Database (LTAFDB), and Normal Sinus Rhythm Database (NSRDB).