Heart Diseases Diagnosis Basen on ECG Harmonic Analysis and Pattern Classification

ISBN

Formato digital
979-13-87837-54-9

Fecha de publicación

06-10-2025

Licencia

D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero

Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.

PDF
Volver

Alejandro Vidales Esquivel
Universidad Michoacana de San Nicolás de Hidalgo
0009-0009-7213-9435
Fernando Ornelas Tellez
Universidad Michoacana de San Nicolás de Hidalgo
0000-0002-6428-8184
José Ortiz Bejar
Universidad Michoacana de San Nicolás de Hidalgo
0000-0001-8023-8008

Acerca de

Heart diseases have been a critical issue to deal with to improve people’s health. Medical research and technology are being developed to obtain accurate diagnoses and treatments. This paper contributes to the design of an automated diagnosis system to classify electrocardiogram (ECG) signals to detect cardiac diseases. The proposed diagnosis system is based on the Fourier series analysis, which uses a dynamical state observer to instantaneously obtain salient features and patterns from the ECG harmonic content in real-time, whose information is classified through a K-Nearest neighbor algorithm (KNN), named as classifier, which determines the possible disease. The ECG signals used in this paper are obtained from the free online available PhysioNet databases, which contain information that can be used for the diagnosis and classification of healthy patients, arrhythmia cases, myocardial infarction, and heart failure. The proposed automated procedure is 93% effective in disease detection for the explored databases, highlighting its potential as a classification tool for ECG-based diagnosis. 

Referencias

Physionet, https://physionet.org/
Brian D. O. Anderson, John B. Moore: Optimal Control: Linear Quadratic Meth- ods. Prentice-Hall, Englewood Cliffs, NJ, USA (1990)
Dang, H., Sun, M., Zhang, G., Qi, X., Zhou, X., Chang, Q.: Deep arrhythmia- diagnosis network for atrial fibrillation classification using electrocardiogram sig- nals. IEEE Access (2019)
Fawcett, T.: Introduction to roc analysis. Pattern Recognition Letters 27, 861–874 (06 2006). https://doi.org/10.1016/j.patrec.2005.10.010
G., S., Vardhan, V.: Multi-class classification using mixtures of univariate and multivariate roc curves. Journal of Biostatistics and Epidemiology 8 (04 2022). https://doi.org/10.18502/jbe.v8i2.10418
Guyton, Hall: Textbook of Medical Physiology. Saunders/Elsevier, twelfth edn. (2011)
Han, J., Kamber, M., Pei, J.: Data mining concepts and techniques. Morgan Kauf- mann Publishers, Waltham, Mass., 3rd edn. (2012)
Ismail, S.N.M.S., Aziz, N.A.A., Ibrahim, S.Z., Nawawi, S.W., Alelyani, S., Mohana, M., Chun, L.C.: Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system. F1000Research 10(1114), 1114 (2021)
Losada, D., Gómez, J., Vela, J.: Classification of ecg signals using machine learn- ing techniques. Academic Journal of Interdisciplinary Studies 13, 92 (05 2024). https://doi.org/10.36941/ajis-2024-0067

Carrito de compra