Noninvasive human red blood cell counting based on dynamic spectrum

Noninvasive and real-time human red blood cell (RBC) counting has high application value in the process of surgery, basic medical research, disease diagnosis and daily management. In this paper, dynamic spectrum (DS) theory was applied to noninvasive human RBC counting under the guidance of M + N theory, meanwhile one-by-one elimination method was used to remove the redundant information and increase the signal-to-noise ratio (SNR) of DS for the first time. To illustrate the effectiveness of this method, DS of 317 volunteers were analyzed. According to the PLS modeling effect, the correlation coefficient of prediction set (Rp) is 0.893, the root mean square error of prediction set (RMSEP) is 0.196 x 10(12)/L, the correlation coefficient of calibration set (Rc) is 0.926 and the root mean square error of calibration set (RMSEC) is 0.180 x 10(12)/L. This paper suggests that the DS theory has the potential to realize clinical application of noninvasive human RBC counting.

Automated summary

This study applied dynamic spectrum theory to noninvasive human RBC counting, utilized one-by-one elimination method to improve signal-to-noise ratio, and validated the effectiveness with 317 volunteers. The results suggest the potential of clinical application for noninvasive human RBC counting using DS theory.