Reconstruction method for radial artery pulse wave based on deep learning
Objective To propose a reconstruction method based on deep learning for addressing the challenge of reconstructing radial artery pulse wave from fingertip pulse wave.Methods A four-channel data acquisition system PowerLab was used to non-invasively acquire finger pulse waves and radial artery pulse waves.The noise source in the pulse wave signals were analyzed,and the stable signal waveforms were obtained after baseline removal,wavelet transform denoising,and normalization preprocessing.The structure and parameters of the variational auto-encoder(VAE)network model were designed.The model was trained using 10-fold cross-validation on data from 744 subjects to establish a prediction model for radial artery pulse waves;and the VAE network model was optimized by adjusting hyperparameter settings of learning rate,dropout,and regularization term.Results The results from the reconstruction and synchronous detection of radial artery pulse waves in 186 subjects showed that for reconstructing radial artery pulse waves from low-and high-resistance fingertip pulse waves,the 5%K difference,20%K difference,total variance of K difference,and FIT were 49.10%,96.70%,89.74,and 75.80%when using VAE network model,and those were 48.50%,94.50%,73.74,and 66.30%when using VAE optimization model.Conclusion The VAE network model and its optimization method can be used for radial artery pulse wave reconstruction,with high reconstruction accuracy,strong robustness and generalization ability.
deep learningpulse wavewave reconstructionmodel optimizationvariational auto-encoder
NETL
NSTL
万方数据
维普
