Premature Ventricular Contraction Recognition using a Fuzzy Maximum Approaching Degree

Authors

  • Eder Pereira Neves Universidade Estadual Paulista, Universidade Estadual de Mato Grosso do Sul
  • Bruno Rodrigues de Oliveira Pantanal Editora
  • Marco Aparecido Queiroz Duarte Universidade Estadual de Mato Grosso do Sul
  • Jozue Vieira Filho Universidade Estadual Paulista

DOI:

https://doi.org/10.33837/msj.v6i1.1591

Keywords:

Premature Ventricular Contraction, Geometrical Attributes, Fuzzy Maximum Approaching Degree

Abstract

This work presents a new methodology for ventricular premature contraction arrhythmias recognition using a set of geometrical attributes recently proposed and a fuzzy maximum approaching degree.  Pattern models based on triangular and trapezoidal membership functions are proposed and a committee comprising these functions is composed using some statistical data, beyond a mechanism for manual selection of attributes and automatic weighting for each attribute. The obtained results show the efficiency and validity of the proposed approach, with 99.07%, 98.36% and 99.79% of accuracy, sensibility and specificity, respectively, as good as the ones obtained by the state-of-art methods.

References

Barros, L. C., & Bassanezi, R. C. (2006). Topics of fuzzy logic and biomathematics. Campinas: IMECC-UNICAMP.

Garcia, T. B., & Garcia, D. J. (2019). Arrhythmia Recognition: The Art of Interpretation: The Art of Interpretation. Jones & Bartlett Learning.

Gharieb, R. R., Massoud, M., Nady, S., & Moness, M. (2016, December). Fuzzy c-means in features space of teager-kaiser energy of continuous wavelet coefficients for detection of PVC beats in ECG. In2016 8th Cairo International Biomedical Engineering Conference (CIBEC)(pp. 72-75). IEEE.

Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), e215-e220.

Guyton, A. C., & Hall, J. E. (2006). Treaty of medical physiology. Elsevier.

Latchamsetty, R., & Bogun, F. (2015). Premature ventricular complexes and premature ventricular complex induced cardiomyopathy. Current Problems in Cardiology, 40(9), 379-422.

Oliveira, B. R., de Abreu, C. C. E., Duarte, M. A. Q., & Vieira Filho, J. (2019). Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection. Computer methods and programs in biomedicine, 169, 59-69.

Ross, T. J. (2017). Fuzzy logic with engineering applications. John Wiley & Sons.

Santos, J. D., Meira, K. C., Camacho, A. R., Salvador, P. T. C. D. O., Guimarães, R. M., Pierin, Â. M. G., & Freire, F. H. M. D. A. (2018). Mortality due to acute myocardial infarction in Brazil and its geographical regions: analyzing the effect of age-period-cohort. Ciência & Saúde Coletiva, 23, 1621-1634.

Shyu, L. Y., Wu, Y. H., & Hu, W. (2004). Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG.IEEE Transactions on Biomedical Engineering,51(7), 1269-1273.

Yeh, Y. C. (2012). An Analysis of ECG for Determining Heartbeat Case by Using the Principal Component Analysis and Fuzzy Logic. International Journal of Fuzzy Systems, 14 (2).

Zadeh, L. A. (2015). Fuzzy logic–a personal perspective, Fussy Sets and Systems, 281, 4-20.

Downloads

Published

2023-04-10

How to Cite

Neves, E. P., Oliveira, B. R. de, Duarte, M. A. Q., & Vieira Filho, J. (2023). Premature Ventricular Contraction Recognition using a Fuzzy Maximum Approaching Degree. Multi-Science Journal, 6(1), 7–14. https://doi.org/10.33837/msj.v6i1.1591

Issue

Section

Biological and Health Sciences

Most read articles by the same author(s)