[Application of wavelet transform on improving detecting precision of the non-invasive blood components measurement based on dynamic spectrum method].

Time-varying noises in spectra collection process have influence on the prediction accuracy of quantitative calibration in the non-invasive blood components measurement which is based on dynamic spectrum (DS) method. By wavelet transform, we focused on the absorbance wave of fingertip transmission spectrum in pulse frequency band. Then we increased the signal to noise ratio of DS data, and improved the detecting precision of quantitative calibration. After carrying out spectrum data continuous acquisition of the same subject for 10 times, we used wavelet transform de-noising to increase the average correlation coefficient of DS data from 0.979 6 to 0.990 3. BP neural network was used to establish the calibration model of subjects' blood components concentration values against dynamic spectrum data of 110 volunteers. After wavelet transform de-noising, the correlation coefficient of prediction set increased from 0.677 4 to 0.846 8, and the average relative error was decreased from 15.8% to 5.3%. Experimental results showed that the introduction of wavelet transform can effectively remove the noise in DS data, improve the detecting precision, and accelerate the development of non-invasive blood components measurement based on DS method.