Modeling the Electrical Activity of the Heart Via Transfer Functions and Genetic Algorithms

Overview

Journal Biomimetics (Basel)

Date 2024 May 24

PMID 38786509

Authors

Omar Rodriguez-Abreo

Mayra Cruz-Fernandez

Carlos Fuentes-Silva

Mario A Quiroz-Juarez

Jose L Aragon

Affiliations

Soon will be listed here.

Abstract

Although healthcare and medical technology have advanced significantly over the past few decades, heart disease continues to be a major cause of mortality globally. Electrocardiography (ECG) is one of the most widely used tools for the detection of heart diseases. This study presents a mathematical model based on transfer functions that allows for the exploration and optimization of heart dynamics in Laplace space using a genetic algorithm (GA). The transfer function parameters were fine-tuned using the GA, with clinical ECG records serving as reference signals. The proposed model, which is based on polynomials and delays, approximates a real ECG with a root-mean-square error of 4.7% and an R2 value of 0.72. The model achieves the periodic nature of an ECG signal by using a single periodic impulse input. Its simplicity makes it possible to adjust waveform parameters with a predetermined understanding of their effects, which can be used to generate both arrhythmic patterns and healthy signals. This is a notable advantage over other models that are burdened by a large number of differential equations and many parameters.

References

Motta S, Pappalardo F . Mathematical modeling of biological systems. Brief Bioinform. 2012; 14(4):411-22. DOI: 10.1093/bib/bbs061. View

Peirlinck M, Costabal F, Yao J, Guccione J, Tripathy S, Wang Y . Precision medicine in human heart modeling : Perspectives, challenges, and opportunities. Biomech Model Mechanobiol. 2021; 20(3):803-831. PMC: 8154814. DOI: 10.1007/s10237-021-01421-z. View

Ryzhii E, Ryzhii M . A heterogeneous coupled oscillator model for simulation of ECG signals. Comput Methods Programs Biomed. 2014; 117(1):40-9. DOI: 10.1016/j.cmpb.2014.04.009. View

Sayadi O, Shamsollahi M, Clifford G . Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model. Physiol Meas. 2010; 31(10):1309-29. PMC: 3148951. DOI: 10.1088/0967-3334/31/10/002. View

Trudel M, Dube B, Potse M, Gulrajani R, Leon L . Simulation of QRST integral maps with a membrane-based computer heart model employing parallel processing. IEEE Trans Biomed Eng. 2004; 51(8):1319-29. DOI: 10.1109/TBME.2004.827934. View

Jaeger K, Tveito A . Deriving the Bidomain Model of Cardiac Electrophysiology From a Cell-Based Model; Properties and Comparisons. Front Physiol. 2022; 12:811029. PMC: 8782150. DOI: 10.3389/fphys.2021.811029. View

Quiroz-Juarez M, Jimenez-Ramirez O, Vazquez-Medina R, Brena-Medina V, Aragon J, Barrio R . Generation of ECG signals from a reaction-diffusion model spatially discretized. Sci Rep. 2019; 9(1):19000. PMC: 6908715. DOI: 10.1038/s41598-019-55448-5. View

Nowbar A, Gitto M, Howard J, Francis D, Al-Lamee R . Mortality From Ischemic Heart Disease. Circ Cardiovasc Qual Outcomes. 2019; 12(6):e005375. PMC: 6613716. DOI: 10.1161/CIRCOUTCOMES.118.005375. View

Silvani A, Calandra-Buonaura G, Dampney R, Cortelli P . Brain-heart interactions: physiology and clinical implications. Philos Trans A Math Phys Eng Sci. 2016; 374(2067). DOI: 10.1098/rsta.2015.0181. View

10.

Quiroz-Juarez M, Rosales-Juarez J, Jimenez-Ramirez O, Vazquez-Medina R, Aragon J . ECG Patient Simulator Based on Mathematical Models. Sensors (Basel). 2022; 22(15). PMC: 9370912. DOI: 10.3390/s22155714. View

11.

Prusty M, Pandey T, Lekha P, Lellapalli G, Gupta A . Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals. Sci Rep. 2024; 14(1):2633. PMC: 10834984. DOI: 10.1038/s41598-024-53107-y. View

12.

Moody G, Mark R . The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag. 2001; 20(3):45-50. DOI: 10.1109/51.932724. View

13.

Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov P, Mark R . PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000; 101(23):E215-20. DOI: 10.1161/01.cir.101.23.e215. View

14.

Quiroz-Juarez M, Jimenez-Ramirez O, Aragon J, Del Rio-Correa J, Vazquez-Medina R . Periodically kicked network of RLC oscillators to produce ECG signals. Comput Biol Med. 2018; 104:87-96. DOI: 10.1016/j.compbiomed.2018.05.017. View

15.

McSharry P, Clifford G, Tarassenko L, Smith L . A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng. 2003; 50(3):289-94. DOI: 10.1109/TBME.2003.808805. View