Nonlinear model for ECG R-R interval variation using genetic programming approach

Authors:
Yun Seok Chang;Kwang Suk Park;Bo Yeon Kim
Affiliations:
Department of Computer Engineering, Daejin University, Pocheon, Republic of Korea;Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, Republic of Korea;Department of Electrical and Computer Engineering, Kangwon National University, Chuncheon, Republic of Korea
Venue:
Future Generation Computer Systems
Year:
2005

Citing 4
Cited 2

Extracting qualitative dynamics from experimental data

Physica D
Chaos: making a new science

Chaos: making a new science
Genetic programming II: automatic discovery of reusable programs

Genetic programming II: automatic discovery of reusable programs
Modeling of the nonlinear sequences based on the modified genetic algorithm

Nonlinear Analysis: Theory, Methods & Applications

Nonlinear characteristics analysis on 3-channel arterial pulse waveform for fast heart rate algorithm

ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Application of genetic programming for modelling of material characteristics

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a nonlinear system modeling method, which predicts characteristics of the ECG R-R interval variation. For determining model equation, we adopted a genetic programming method in which the chromosome represents the model equation consisting of time-delayed variables, constants, and four arithmetic operators, and determines the fitness function. By genetic programming, sequences of regressive nonlinear equations are produced and evolved until the finding of the optimal model equation, which could simulate the spectral, statistical and nonlinear behavior of the given R-R interval dynamics. Experimental results showed that the evolutionary approach could find the equation which simulates the spectral and chaotic dynamics of the given signal. Therefore, the proposed evolutionary approach is useful for the system identification of the nonlinear biological system.