Machine Learning for Localization of Premature Ventricular Contraction Origins: A Review.
Premature ventricular contraction (PVC) is one of the most common arrhythmias, originating from ectopic beats in the ventricles. Precision in localizing the origin of PVCs has long been a focal point in electrophysiology research. Machine learning (ML) has developed rapidly in the past two decades w...
| Publicado en: | Pacing & Clinical Electrophysiology Vol. 47; no. 11; pp. 1481 - 1492 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas pictorial review tables/charts tracings Journal Article |
| Publicado: |
Wiley-Blackwell
Nov2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180680449&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180680449 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01478389 4F8 jtl: Pacing & Clinical Electrophysiology issn: 01478389 maglogo: Y pubinfo: dt: Nov2024 vid: 47 iid: 11 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 180680449 180385350 180680449 180680449 10.1111/pace.15089 180680449 ppf: 1481 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Machine Learning for Localization of Premature Ventricular Contraction Origins: A Review. aug: au: Yang, Rui Wang, Yiwen Wang, Yanan Feng, Xujian Yang, Cuiwei affil: Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, P.R. China sug: subj: Premature Ventricular Contractions Diagnosis Machine Learning Electrocardiography Reference Values Convolutional Neural Networks Support Vector Machine Quality Improvement Magnetic Resonance Imaging Premature Ventricular Contractions Physiopathology ab: Premature ventricular contraction (PVC) is one of the most common arrhythmias, originating from ectopic beats in the ventricles. Precision in localizing the origin of PVCs has long been a focal point in electrophysiology research. Machine learning (ML) has developed rapidly in the past two decades with increasingly widespread applications. With the increase of clinical data such as electrocardiograms (ECGs), computed tomography (CT), and magnetic resonance imaging (MRI), ML and its subfields, deep learning (DL), have become powerful analytical tools, playing an increasingly important role in electrophysiological research. In this review, we mainly provide an overview of the development of ML in the localization of PVC origins, including its applications, advantages, disadvantages, and future research directions. This information is intended to serve as a reference for clinicians and researchers, aiding them in better‐utilizing ML techniques for the diagnosis and study of PVC origins. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial review tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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