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Recognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and Machine Learning Techniques
dc.rights.license | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.contributor.author | Ayala-Cucas H.A. | |
dc.contributor.author | Mora-Piscal E.A. | |
dc.contributor.author | Mayorca-Torres D. | |
dc.contributor.author | León-Salas A.J. | |
dc.contributor.author | Peluffo-Ordoñez D.H. | |
dc.contributor.editor | Botto-Tobar M. | |
dc.contributor.editor | Gómez O.S. | |
dc.contributor.editor | Rosero Miranda R. | |
dc.contributor.editor | Luna-Encalada W. | |
dc.contributor.editor | Díaz Cadena A. | |
dc.contributor.other | 4th International Conference on Advances in Emerging Trends and Technologies, ICAETT 2022 | |
dc.date.accessioned | 2024-12-02T20:16:01Z | |
dc.date.available | 2024-12-02T20:16:01Z | |
dc.date.issued | 2023 | |
dc.identifier.isbn | 978-303125941-8 | |
dc.identifier.issn | 23673370 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14112/29009 | |
dc.description.abstract | Cardiac 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.sponsorship | The authors would like to acknowledge the valuable support given by the SDAS Research Group (https://sdas-group.com/). | |
dc.format | 12 | |
dc.format.medium | Recurso electrónico | |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Springer Science and Business Media Deutschland GmbH | |
dc.rights.uri | Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) | |
dc.source | Lecture Notes in Networks and Systems | |
dc.source | Lect. Notes Networks Syst. | |
dc.source | Scopus | |
dc.title | Recognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and Machine Learning Techniques | |
datacite.contributor | Grupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia | |
datacite.contributor | Departamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, C/Periodista Daniel Saucedo Aranda s/n, Granada, 18071, Spain | |
datacite.contributor | Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir, Morocco | |
datacite.contributor | Ayala-Cucas H.A., Grupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia | |
datacite.contributor | Mora-Piscal E.A., Grupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia | |
datacite.contributor | Mayorca-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.contributor | León-Salas A.J., Departamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, C/Periodista Daniel Saucedo Aranda s/n, Granada, 18071, Spain | |
datacite.contributor | Peluffo-Ordoñez D.H., Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir, Morocco | |
datacite.contributor | 4th International Conference on Advances in Emerging Trends and Technologies, ICAETT 2022 | |
datacite.rights | http://purl.org/coar/access_right/c_abf2 | |
oaire.resourcetype | http://purl.org/coar/resource_type/c_c94f | |
oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | |
dc.contributor.contactperson | D. Mayorca-Torres | |
dc.contributor.contactperson | Grupo de investigación de Ingeniería Mecatrónica, Universidad Mariana, Pasto, Colombia | |
dc.contributor.contactperson | email: dmayorca@umarlana.edu.co | |
dc.contributor.sponsor | Shandong Academy of Sciences, SDAS | |
dc.identifier.doi | 10.1007/978-3-031-25942-5_1 | |
dc.identifier.instname | Universidad Mariana | |
dc.identifier.reponame | Repositorio Clara de Asis | |
dc.identifier.url | https://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.citationendpage | 15 | |
dc.relation.citationstartpage | 3 | |
dc.relation.citationvolume | 619 LNNS | |
dc.relation.conferencedate | 26 October 2022 through 28 October 2022 | |
dc.relation.conferenceplace | Riobamba | |
dc.relation.iscitedby | 0 | |
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dc.relation.references | Rodriguez-Sotelo J.L., Peluffo-Ordonez D., Cuesta-Frau D., Castellanosdominguez G., Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering, Comput. Methods Programs Biomed., 108, 1, pp. 250-261, (2012) | |
dc.relation.references | Sahoo S., Subudhi A., Dash M., Sabut S., Automatic classification of cardiac arrhythmias based on hybrid features and decision tree algorithm, Int. J. Autom. Comput., 17, 4, pp. 551-561, (2020) | |
dc.relation.references | Sharma P., Dinkar S.K., Gupta D.V., A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals, Neural Comput. Appl., 33, 19, pp. 13123-13143, (2021) | |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
dc.subject.keywords | Cardiac arrhythmia | |
dc.subject.keywords | Electrocardiogram (ECG) | |
dc.subject.keywords | Feature extraction | |
dc.subject.keywords | Machine learning | |
dc.subject.keywords | Performance measures | |
dc.subject.keywords | Biomedical signal processing | |
dc.subject.keywords | Cardiology | |
dc.subject.keywords | Classification (of information) | |
dc.subject.keywords | Decision trees | |
dc.subject.keywords | Digital filters | |
dc.subject.keywords | Discrete wavelet transforms | |
dc.subject.keywords | Diseases | |
dc.subject.keywords | Feature extraction | |
dc.subject.keywords | Heart | |
dc.subject.keywords | Learning algorithms | |
dc.subject.keywords | Nearest neighbor search | |
dc.subject.keywords | Signal reconstruction | |
dc.subject.keywords | Cardiac arrhythmia | |
dc.subject.keywords | Discrete-wavelet-transform | |
dc.subject.keywords | Electrical impulse | |
dc.subject.keywords | Electrocardiogram | |
dc.subject.keywords | Electrocardiogram signal | |
dc.subject.keywords | Features extraction | |
dc.subject.keywords | Follow up | |
dc.subject.keywords | Machine learning techniques | |
dc.subject.keywords | Machine-learning | |
dc.subject.keywords | Performance measure | |
dc.subject.keywords | Electrocardiograms | |
dc.type.driver | info:eu-repo/semantics/conferenceObject | |
dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
dc.type.redcol | http://purl.org/redcol/resource_type/ARTDATA | |
dc.type.spa | Contribución a congreso / Conferencia |
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