Coupled Tensor Model of Atrial Fibrillation ECG

Abstract

Atrial fibrillation (AF) is the most frequent cardiac arrhythmia diagnosed in clinical practice, identified by an uncoordinated and irregular atrial depolarization. However, its electrophysiological mechanisms are still not clearly understood, increasing the intensive clinical research into this challenging cardiac condition in the past few years. The noninvasive extraction of the atrial activity (AA) from multi-lead electrocardiogram (ECG) recordings by signal processing techniques has helped in better understanding this complex arrhythmia. In particular, tensor decomposition techniques have proven to be powerful tools in this task, overcoming the limitations of matrix factorization methods. Exploring the spatial as well as the temporal diversity of ECG recordings, this contribution puts forward a novel noninvasive AA extraction method that models consecutive AF ECG segments as a coupled block-term tensor decomposition, assuming that they share the same spatial signatures. Experiments on synthetic and real data, the latter acquired from persistent AF patients, validate the proposed coupled tensor approach, which provides satisfactory performance with reduced computational cost.