HRV and BPV neural network model with wavelet based algorithm calibration

摘要

The heart rate and blood pressure power spectrum, especially the power of the low frequency (LF) and high frequency (HF) components, have been widely used in the last decades for quantification of both autonomic function and respiratory activity. Discrete Wavelet Transform (DWT) and the Fast Fourier Transform (FFT) represent important tolls in this field. The paper presents a new solution for LF and HF evaluation that combines the Daubechies DWT with neural processing techniques. Several types of neural networks (Radial Basis Function and Multilayer Perceptron) capable of evaluating LF and HF values were designed and implemented. The training values to design the network were obtained after heart rate and blood pressure wavelets processing. The designed neural structures assure a faster evaluation tool of the sympathetic and parasympathetic autonomic nervous system control of cardiovascular function.