Tensor-based detection of paroxysmal and persistent atrial fibrillation from multi-channel ECG

Hanie Moghaddasi, Alle Jan van der Veen, Natasja M.S. de Groot, Borbála Hunyadi

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
Pages1155-1159
Number of pages5
ISBN (Electronic)9789082797053
DOIs
Publication statusPublished - 24 Jan 2021
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020

Publication series

SeriesEuropean Signal Processing Conference
Volume2021-January
ISSN2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
Country/TerritoryNetherlands
CityAmsterdam
Period24/08/2028/08/20

Bibliographical note

Funding Information:
This research was funded in part by the Medical Delta Cardiac Arrhythmia Lab (CAL).

Publisher Copyright:
© 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

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