Non-invasive detection of the content of white blood cells in the blood of humans based on dynamic spectrum

Abstract

Objective. Changes in white blood cell content have been shown to be useful in determining whether the body is in a healthy state. We propose an improved data processing and modeling approach, which helps to accommodate blood component content detection and improve prediction accuracy. A pproach. In this experiment, the finger-end transmission method was used for spectral measurement, and we collected a total of 440 sample data. In this paper, we first use the method of CEEMDAN combined with wavelet threshold to denoise the PPG signal, and then use the integral method to extract the spectral features, which makes up for the defects of the single-edge method using incomplete data and the deviation of the slope of the rising segment from the actual signal. We further improve the screening of samples and wavelengths, and used PLS regression modeling combine the double nonlinear correction method to build the most stable and universal model. Main results. The model has been applied to 332 subjects’ finger transmission spectral data to predict the concentration of leukocytes. The correlation coefficient of the final training set result was 0.927, and the root mean square error (RMSE) is 0.569×109 l−1, the correlation coefficient of the prediction set result is 0.817, and the RMSE is 0.826×109 l−1, which proves the practicability of the proposed method. Significance. We propose a non-invasive method for detecting leukocyte concentration in blood that can also be generalized to detect other blood components.