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dc.rights.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.authorAyala-Cucas H.A.
dc.contributor.authorMora-Piscal E.A.
dc.contributor.authorMayorca-Torres D.
dc.contributor.authorLeón-Salas A.J.
dc.contributor.authorPeluffo-Ordoñez D.H.
dc.contributor.editorBotto-Tobar M.
dc.contributor.editorGómez O.S.
dc.contributor.editorRosero Miranda R.
dc.contributor.editorLuna-Encalada W.
dc.contributor.editorDíaz Cadena A.
dc.contributor.other4th International Conference on Advances in Emerging Trends and Technologies, ICAETT 2022
dc.date.accessioned2024-12-02T20:16:01Z
dc.date.available2024-12-02T20:16:01Z
dc.date.issued2023
dc.identifier.isbn978-303125941-8
dc.identifier.issn23673370
dc.identifier.urihttps://hdl.handle.net/20.500.14112/29009
dc.description.abstractCardiac arrhythmias are heart rhythm problems that usually occur when the electrical impulses coordinated with the heartbeat do not work correctly. For this reason, detecting abnormalities in an electrocardiogram (ECG) plays a vital role in patient follow-up. Due to the presence of noise, the irregularity of the heartbeat, and the nonstationary nature of ECG signals, their interpretation can be difficult, requiring the use of advanced computer systems to support the diagnosis of cardiac disorders. Therefore, the development of assisted ECG analysis systems is a current topic of study, and the main challenge is to achieve adequate accuracy for application in the clinical setting. Therefore, this article describes a software tool for classifying ECG samples into the main classes of cardiac arrhythmias by removing noise from the ECG signal at the preprocessing stage using conventional digital filters, the location of the QRS complex is essential for the identification of the ECG signal. Therefore, the position and amplitude of the R peaks are determined in the segmentation stage. Then the selection of the most relevant features of the ECG signal is performed using the discrete wavelet transform (DWT). The ability of the extracted features to differentiate between different classes of data is tested using machine learning techniques such as k-Nearest Neighbors, Neural Networks, and Decision Trees with 10-fold cross-validation. These methods are evaluated and tested with the MIT-BIH arrhythmia database, achieving the best accuracy of 98.54% using the k-Nearest Neighbors classifier. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.description.sponsorshipThe authors would like to acknowledge the valuable support given by the SDAS Research Group (https://sdas-group.com/).
dc.format12
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights.uriAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.sourceLecture Notes in Networks and Systems
dc.sourceLect. Notes Networks Syst.
dc.sourceScopus
dc.titleRecognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and Machine Learning Techniques
datacite.contributorGrupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia
datacite.contributorDepartamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, C/Periodista Daniel Saucedo Aranda s/n, Granada, 18071, Spain
datacite.contributorModeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir, Morocco
datacite.contributorAyala-Cucas H.A., Grupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia
datacite.contributorMora-Piscal E.A., Grupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia
datacite.contributorMayorca-Torres D., Grupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia, Departamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, C/Periodista Daniel Saucedo Aranda s/n, Granada, 18071, Spain
datacite.contributorLeón-Salas A.J., Departamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, C/Periodista Daniel Saucedo Aranda s/n, Granada, 18071, Spain
datacite.contributorPeluffo-Ordoñez D.H., Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir, Morocco
datacite.contributor4th International Conference on Advances in Emerging Trends and Technologies, ICAETT 2022
datacite.rightshttp://purl.org/coar/access_right/c_abf2
oaire.resourcetypehttp://purl.org/coar/resource_type/c_c94f
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.contributor.contactpersonD. Mayorca-Torres
dc.contributor.contactpersonGrupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia
dc.contributor.contactpersonemail: dmayorca@umarlana.edu.co
dc.contributor.sponsorShandong Academy of Sciences, SDAS
dc.identifier.doi10.1007/978-3-031-25942-5_1
dc.identifier.instnameUniversidad Mariana
dc.identifier.reponameRepositorio Clara de Asis
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151045801&doi=10.1007%2f978-3-031-25942-5_1&partnerID=40&md5=774ab0bf3ee73179f5d0635c0c776f89
dc.relation.citationendpage15
dc.relation.citationstartpage3
dc.relation.citationvolume619 LNNS
dc.relation.conferencedate26 October 2022 through 28 October 2022
dc.relation.conferenceplaceRiobamba
dc.relation.iscitedby0
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsCardiac arrhythmia
dc.subject.keywordsElectrocardiogram (ECG)
dc.subject.keywordsFeature extraction
dc.subject.keywordsMachine learning
dc.subject.keywordsPerformance measures
dc.subject.keywordsBiomedical signal processing
dc.subject.keywordsCardiology
dc.subject.keywordsClassification (of information)
dc.subject.keywordsDecision trees
dc.subject.keywordsDigital filters
dc.subject.keywordsDiscrete wavelet transforms
dc.subject.keywordsDiseases
dc.subject.keywordsFeature extraction
dc.subject.keywordsHeart
dc.subject.keywordsLearning algorithms
dc.subject.keywordsNearest neighbor search
dc.subject.keywordsSignal reconstruction
dc.subject.keywordsCardiac arrhythmia
dc.subject.keywordsDiscrete-wavelet-transform
dc.subject.keywordsElectrical impulse
dc.subject.keywordsElectrocardiogram
dc.subject.keywordsElectrocardiogram signal
dc.subject.keywordsFeatures extraction
dc.subject.keywordsFollow up
dc.subject.keywordsMachine learning techniques
dc.subject.keywordsMachine-learning
dc.subject.keywordsPerformance measure
dc.subject.keywordsElectrocardiograms
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTDATA
dc.type.spaContribución a congreso / Conferencia


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