000065316 001__ 65316
000065316 005__ 20240912131720.0
000065316 0247_ $$2doi$$a10.1098/rsif.2017.0821
000065316 0248_ $$2sideral$$a104237
000065316 037__ $$aART-2018-104237
000065316 041__ $$aspa
000065316 100__ $$aLyon, Aurore
000065316 245__ $$aComputational techniques for ECG analysis and interpretation in light of their contribution to medical advances
000065316 260__ $$c2018
000065316 5060_ $$aAccess copy available to the general public$$fUnrestricted
000065316 5203_ $$aWidely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
000065316 536__ $$9info:eu-repo/grantAgreement/EC/H2020/675451/EU/A Centre of Excellence in Computational Biomedicine/CompBioMed$$9info:eu-repo/grantAgreement/ES/DGA/Grupo Consolidado BSICoS$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 675451-CompBioMed$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R
000065316 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000065316 590__ $$a3.224$$b2018
000065316 591__ $$aMULTIDISCIPLINARY SCIENCES$$b14 / 68 = 0.206$$c2018$$dQ1$$eT1
000065316 592__ $$a1.627$$b2018
000065316 593__ $$aBiochemistry$$c2018$$dQ1
000065316 593__ $$aBioengineering$$c2018$$dQ1
000065316 593__ $$aBiotechnology$$c2018$$dQ1
000065316 593__ $$aBiomedical Engineering$$c2018$$dQ1
000065316 593__ $$aBiophysics$$c2018$$dQ1
000065316 593__ $$aBiomaterials$$c2018$$dQ1
000065316 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000065316 700__ $$aMincholé, Ana
000065316 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez, Juan Pablo$$uUniversidad de Zaragoza
000065316 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000065316 700__ $$aRodríguez, Blanca
000065316 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000065316 773__ $$g15, 138 (2018), 20170821 [18 pp.]$$pJ. R. Soc. Interface$$tJournal of the Royal Society Interface$$x1742-5689
000065316 85641 $$uhttps://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0821$$zTexto completo de la revista
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000065316 951__ $$a2024-09-12-13:15:35
000065316 980__ $$aARTICLE