Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation.

Details

Serval ID
serval:BIB_00774A75E331
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation.
Journal
BMC medical informatics and decision making
Author(s)
Prudat Y., Luca A., Yazdani S., Derval N., Jaïs P., Roten L., Berte B., Pruvot E., Vesin J.M., Pascale P.
ISSN
1472-6947 (Electronic)
ISSN-L
1472-6947
Publication state
Published
Issued date
28/08/2022
Peer-reviewed
Oui
Volume
22
Number
1
Pages
225
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
The automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex and/or fractionated signals. We propose a new method which provides reliable automatic extraction of intracardiac AAs recorded within the pulmonary veins during AF and an accurate estimation of their local activation times.
First, two recently developed algorithms were evaluated and optimized on 118 recordings of pulmonary vein IEGM taken from 35 patients undergoing ablation of persistent AF. The adaptive mathematical morphology algorithm (AMM) uses an adaptive structuring element to extract AAs based on their morphological features. The relative-energy algorithm (Rel-En) uses short- and long-term energies to enhance and detect the AAs in the IEGM signals. Second, following the AA extraction, the signal amplitude was weighted using statistics of the AA sequences in order to reduce over- and undersensing of the algorithms. The detection capacity of our algorithms was compared with manually annotated activations and with two previously developed algorithms based on the Teager-Kaiser energy operator and the AF cycle length iteration, respectively. Finally, a method based on the barycenter was developed to reduce artificial variations in the activation annotations of complex IEGM signals.
The best detection was achieved using Rel-En, yielding a false negative rate of 0.76% and a false positive rate of only 0.12% (total error rate 0.88%) against expert annotation. The post-processing further reduced the total error rate of the Rel-En algorithm by 70% (yielding to a final total error rate of 0.28%).
The proposed method shows reliable detection and robust temporal annotation of AAs recorded within pulmonary veins in AF. The method has low computational cost and high robustness for automatic detection of AAs, which makes it a suitable approach for online use in a procedural context.
Keywords
Algorithms, Atrial Fibrillation, Electrophysiologic Techniques, Cardiac, Humans, Pulmonary Veins, Activation detection, Atrial fibrillation, Biomedical signal processing, Intracardiac electrograms, Non-linear signal processing
Pubmed
Open Access
Yes
Create date
05/09/2022 9:35
Last modification date
09/09/2022 6:38
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