Evidence map›Paper›PMID 39838437›Full record

ArticleBMC medical informatics and decision making2025

Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

Ling-Chun Sun, Chia-Chiang Lee, Hung-Yen Ke, Chih-Yuan Wei, Ke-Feng Lin, Shih-Sung Lin, Hsin Hsiu, Ping-Nan Chen

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Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ling-Chun SunSchool of Medicine, National Defense Medical Center, Taipei, 11490, Taiwan.
Chia-Chiang LeeGraduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan.
Hung-Yen KeDivision of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, 11490, Taiwan.
Chih-Yuan WeiGraduate Institute of Life Sciences, National Defense Medical Center, Taipei, 11490, Taiwan.
Ke-Feng LinSchool of Public Health, National Defense Medical Center, Taipei, 11490, Taiwan.
Shih-Sung LinDepartment of Computer Science and Information Engineering, Chinese Culture University, Taipei, 11114, Taiwan.
Hsin HsiuGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, 10607, Taipei, Taiwan.
Ping-Nan ChenDepartment of Biomedical Engineering, National Defense Medical Center, Taiwan, No.161, Sec.6, Minchiuan E. Rd., Neihu Dist, Taipei, 11490, Taiwan. g931310@gmail.com.ORCID 0000-0001-6240-4340

Funding

Ministry of National Defense-Medical Affairs Bureau MND-MAB-D-111097;MND-MAB-D-113143
6 · The paper itself

Abstract

backgroundAs the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum.

resultsWe present a MsCWT image-based deep learning machine for AF differentiation. For the training, validation, and test sets, we achieved average accuracies of 97.94%, 97.84%, and 91.32%; and overall F1 scores of 97.13%, 96.86%, and 89.41% respectively. Moreover, AUC ROC curves of over 0.99 were obtained for all classes in the training and validation sets; and were over 0.9679 for the test set.

conclusionsTraining deep learning machines for the classification of AF with MsCWT-based images demonstrated to yield favorable outcomes and achieved superior performance amongst studies utilizing the same dataset. Though minimal, the conversion of signals into wavelet form with MsCWT may drastically improve outcomes not only in future ECG signal studies; but all signal-based diagnostics.

Indexed as

Atrial FibrillationDeep LearningElectrocardiographyWavelet AnalysisHumansAtrial fibrillationConvolutional Neural NetworkMsCWTResNet101

Identifiers

PMID39838437
PMCPMC11752627

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.