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.

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28th European Signal Processing Conference, EUSIPCO 2020
Department of Cardiology

Moghaddasi, H. (Hanie), van der Veen, A.-J. (Alle-Jan), de Groot, N., & 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