Title :
Neural network in signal analysis of heart rate variability
Author :
Jiong Ruan ; Qing Ruan ; Wu, Chen
Author_Institution :
Dept. of Math., FuDan Univ., Shanghai, China
Abstract :
In our study, we use six nonlinear indexes and two linear ones, which are Complexity, Lyapunov Exponent, Predictability, Correlation Dimension, Pioncare Scattergraph, Approximate Entropy; Time and Frequency Domains, respectively, as input parameters of neural networks, i.e. Back-Propagation (BP) networks with only one hidden layer. According to all eight indexes of the subjects given by WuHan TongJi Hospital, we not only introduce a BP network, but improve the algorithm to avoid its proper weakness as well. Neural networks are always regarded as an accurate way to analyze and study the structure of a system. The simulation results reinforce the fact that it is more effective to clinically diagnose cardiovascular diseases by combining the eight indexes with the optimized algorithm in BP networks.
Keywords :
cardiovascular system; medical diagnostic computing; neural nets; BP network; cardiovascular diseases; heart rate variability; neural networks; nonlinear indexes; Artificial neural networks; Biological neural networks; Cardiology; Entropy; Heart rate variability; Humans; Intelligent networks; Neural networks; Neurons; Signal analysis;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1199032