%0 Journal Article %1 Chang:2005:FGCS %A Chang, Yun Seok %A Park, Kwang Suk %A Kim, Bo Yeon %D 2005 %J Future Generation Computer Systems %K algorithms, genetic programming %N 7 %P 1117--1123 %R doi:10.1016/j.future.2004.03.011 %T Nonlinear model for ECG R-R interval variation using genetic programming approach %U http://www.sciencedirect.com/science/article/B6V06-4CVX0RT-1/2/111fea795562435e39023c448749d96a %V 21 %X We propose a nonlinear system modelling 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 behaviour 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.

@article{Chang:2005:FGCS, abstract = {We propose a nonlinear system modelling 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 behaviour 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Chang, Yun Seok and Park, Kwang Suk and Kim, Bo Yeon}, biburl = {https://www.bibsonomy.org/bibtex/2eb5fece8b8f8fdfc951ecdddec0f07c2/brazovayeye}, doi = {doi:10.1016/j.future.2004.03.011}, interhash = {2420f40092d60d27d1d8146db0884ccd}, intrahash = {eb5fece8b8f8fdfc951ecdddec0f07c2}, journal = {Future Generation Computer Systems}, keywords = {algorithms, genetic programming}, month = {July}, number = 7, owner = {wlangdon}, pages = {1117--1123}, timestamp = {2008-06-19T17:37:31.000+0200}, title = {Nonlinear model for {ECG} {R}-{R} interval variation using genetic programming approach}, url = {http://www.sciencedirect.com/science/article/B6V06-4CVX0RT-1/2/111fea795562435e39023c448749d96a}, volume = 21, year = 2005 }