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Atrial Fibrillation Stratification via Fibrillatory Wave Characterization Using the Filter Diagonalization Method

Mishra, S; Rammohan, S; Rajab, KZ; Dhillon, G; Lambiase, P; Hunter, R; Chew, E; (2019) Atrial Fibrillation Stratification via Fibrillatory Wave Characterization Using the Filter Diagonalization Method. In: Proceedings of the 2019 Computing in Cardiology (CinC). (pp. pp. 1-4). IEEE Green open access

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Abstract

We use the Filter Diagonalization Method (FDM), a harmonic inversion technique, to extract f-wave features in electrocardiographic (ECG) traces for atrial fibrillation (AF) stratification. The FDM detects f-wave frequencies and amplitudes at frame sizes of 0.15 seconds. We demonstrate our method on a dataset comprising of ECG recordings from 23 patients (61.65 ± 11.63 years, 78.26% male) before cryoablation; 2 paroxysmal AF, 16 early persistent AF (<12 months duration), and 4 longstanding persistent AF (>12 months duration). Moreover, some of these patients received adenosine to enhance their RR intervals before ablation. Our method extracts features from FDM outputs to train statistical machine learning classifiers. Tenfold cross-validation demonstrates that the Random Forest and Decision Tree models performed best for the pre-ablation without and with adenosine datasets, with accuracy 60.89 ± 0.31% and 59.58% ± 0.04%, respectively. While the results are modest, they demonstrate that f-wave features can be used for AF stratification. The accuracies are similar for the two tests, slightly better for the case without adenosine, showing that the FDM can successfully model short f-waves without the need to concatenate f-wave sequences or adenosine to elongate RR intervals.

Type: Proceedings paper
Title: Atrial Fibrillation Stratification via Fibrillatory Wave Characterization Using the Filter Diagonalization Method
Event: 2019 Computing in Cardiology (CinC)
Location: Singapore
Dates: 8th-11th September 2019
ISBN-13: 978-1-7281-6936-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/CinC49843.2019.9005797
Publisher version: http://www.cinc.org/archives/2019/pdf/CinC2019-353...
Language: English
Additional information: © 2019 Computing in Cardiology. CinC has been an open-access publication, in which copyright in each article is held by its authors, who grant permission to copy and redistribute their work with attribution, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Keywords: Frequency division multiplexing, Feature extraction, Electrocardiography, Harmonic analysis, Power harmonic filters, Decision trees, Diseases
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Clinical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10094256
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