Neural network in signal analysis of heart rate variability

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.