65316 20240912131720.0 doi 10.1098/rsif.2017.0821 sideral 104237 ART-2018-104237 spa Lyon, Aurore Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances 2018 Access copy available to the general public Unrestricted Widely 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. info:eu-repo/grantAgreement/ES/DGA/Grupo Consolidado BSICoS info:eu-repo/grantAgreement/EC/H2020/675451/EU/A Centre of Excellence in Computational Biomedicine/CompBioMed This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 675451-CompBioMed info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.224 2018 MULTIDISCIPLINARY SCIENCES 14 / 68 = 0.206 2018 Q1 T1 1.627 2018 Biochemistry 2018 Q1 Bioengineering 2018 Q1 Biotechnology 2018 Q1 Biomedical Engineering 2018 Q1 Biophysics 2018 Q1 Biomaterials 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Mincholé, Ana Martínez, Juan Pablo Universidad de Zaragoza (orcid)0000-0002-7503-3339 Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-3434-9254 Rodríguez, Blanca 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 15, 138 (2018), 20170821 [18 pp.] J. R. Soc. Interface Journal of the Royal Society Interface 1742-5689 1263462 http://zaguan.unizar.es/record/65316/files/texto_completo.pdf Postprint 2704624 http://zaguan.unizar.es/record/65316/files/texto_completo.jpg?subformat=icon icon Postprint https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0821 Texto completo de la revista oai:zaguan.unizar.es:65316 articulos driver 2024-09-12-13:15:35 ARTICLE