Atrial fibrillation (AF) is the most common arrhythmia in the heart. Two main types of AF are defined as paroxysmal and persistent. In this paper, we present a method to discriminate between the characteristics of paroxysmal and persistent using tensor decompositions of a multi-channel electrocardiogram (ECG) signal. For this purpose, ECG signals are segmented by applying a Hilbert transform on the thresholded signal. Dynamic time warping is used to align the separated segments of each channel and then a tensor is constructed with three dimensions as time, heartbeats and channels. A Canonical polyadic decomposition with rank 2 is computed from this tensor and the resulting loading vectors describe the characteristics of paroxysmal and persistent AF in these three dimensions. The time loading vector reveals the pattern of a single P wave or abnormal AF patterns. The heartbeat loading vector shows whether the pattern is present or absent in a specific beat. The results can be used to distinguish between the patterns in paroxysmal AF and persistent AF.

Atrial fibrillation, Canonical polyadic decomposition, Electrocardiogram, Tensor decomposition,
28th European Signal Processing Conference, EUSIPCO 2020
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Department of Cardiology

Moghaddasi, H. (Hanie), van der Veen, A.-J. (Alle-Jan), de Groot, N.M.S, & Hunyadi, B. (Borbála). (2021). Tensor-based detection of paroxysmal and persistent atrial fibrillation from multi-channel ECG. In European Signal Processing Conference (pp. 1155–1159). doi:10.23919/Eusipco47968.2020.9287718