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dc.creatorMartínez Coq, P.
dc.creatorRey, A.
dc.creatorRosso, O. A.
dc.creatorArmentano, R.
dc.creatorLegnani, W.
dc.date.accessioned2024-03-19T20:27:14Z
dc.date.available2024-03-19T20:27:14Z
dc.date.issued2022-12-05
dc.identifier.citationChaos 32es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12272/9930
dc.description.abstractThe aim of this study is to formulate a new methodology based upon informational tools to detect patients with cardiac arrhythmias. As it is known, sudden death is the consequence of a final arrhythmia, and here lies the relevance of the efforts aimed at the early detection of arrhythmias. The information content in the time series from an electrocardiogram (ECG) signal is conveyed in the form of a probability distribution function, to compute the permutation entropy proposed by Bandt and Pompe. This selection was made seeking its remarkable conceptual simplicity, computational speed, and robustness to noise. In this work, two well-known databases were used, one containing normal sinus rhythms and another one containing arrhythmias, both from the MIT medical databank. For different values of embedding time delay τ normalized permutation entropy and statistical complexity measure are computed to finally represent them on the horizontal and vertical axes, respectively, which define the causal plane H × C. To improve the results obtained in previous works, a feature set composed by these two magnitudes is built to train the following supervised machine learning algorithms: random forest (RF), support vector machine (SVM), and k nearest neighbors (kNN). To evaluate the performance of each classification technique, a 10-fold cross-validation scheme repeated 10 times was implemented. Finally, to select the best model, three quality parameters were computed, namely, accuracy, the area nder the receiver operative characteristic (ROC) curve (AUC), and the F1-score. The results obtained show that the best classification model to detect the ECG coming from arrhythmic patients is RF. The values of the quality parameters were at the same levels reported in the available literature using a larger data set, thus supporting this proposal that uses a very small-sized feature space to train the model later used to classify. Summarizing, the attained results show the possibility to discriminate both groups of patients, with normal sinus rhythm or arrhythmic ECG, showing a promising efficiency in the definition of new markers for the detection of cardiovascular pathologies.es_ES
dc.formatpdfes_ES
dc.language.isoenges_ES
dc.rightsopenAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.rights.uriAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.sourceChaos 32, 123118. (2022)es_ES
dc.subjectConceptses_ES
dc.subjectApplicationses_ES
dc.subjectNew developments and challengeses_ES
dc.titleDetection of cardiac arrhythmia patterns in ECG through H × C planees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holderP. Martínez, A. Rey, A. Rosso, R. Armentano, W. Legnanies_ES
dc.description.affiliationFil: Martínez Coq, P. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Signal and Image Processing Center (CPSI); Argentina.es_ES
dc.description.affiliationFil: Rey, A. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Signal and Image Processing Center (CPSI); Argentina.es_ES
dc.description.affiliationFil: Rosso, O. A. Universidade Federal de Alagoas (UFAL). Physics Institute; Brasil.es_ES
dc.description.affiliationFil: Rosso, O. A. Universidad Nacional de la Plata. Conicet. Instituto de Física (IFLP); Argentina.es_ES
dc.description.affiliationFil: Armentano, R. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Bioengineering Research and Development Group (GIBIO); Argentina.es_ES
dc.description.affiliationFil: Legnani, W. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Signal and Image Processing Center (CPSI); Argentina.es_ES
dc.description.peerreviewedPeer Reviewedes_ES
dc.type.versionpublisherVersiones_ES
dc.rights.useLicencia Creative Commons Atribución- No Comerciales_ES
dc.identifier.doi10.1063/5.0118717


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