An Optimizing Dynamic Spectrum Differential Extraction Method for Noninvasive Blood Component Analysis

Dynamic spectra (DS) can greatly reduce the influence of individual differences and the measurement environment by extracting the absorbance of pulsating blood at multiple wavelengths, and it is expected to achieve noninvasive detection of blood components. Extracting high-quality DS is the prerequisite for improving detection accuracy. This paper proposed an optimizing differential extraction method in view of the deficiency of existing extraction methods. In the proposed method, the sub-dynamic spectrum (sDS) is composed by sequentially extracting the absolute differences of two sample points corresponding to the height of the half peak on the two sides of the lowest point in each period of the logarithm photoplethysmography signal. The study was based on clinical trial data from 231 volunteers. Single-trial extraction method, original differential extraction method, and optimizing differential extraction method were used to extract DS from the volunteers’ experimental data. Partial least squares regression (PLSR) and radial basis function (RBF) neural network were used for modeling. According to the effect of PLSR modeling, by extracting DS using the proposed method, the correlation coefficient of prediction set (Rp) has been improved by 17.33% and the root mean square error of prediction set has been reduced by 7.10% compared with the original differential extraction method. Compared with the single-trial extraction method, the correlation coefficient of calibration set (Rc) has increased from 0.747659 to 0.8244, with an increase of 10.26%, while the correlation coefficient of prediction set (Rp) decreased slightly by 3.22%, much lower than the increase of correction set. The result of the RBF neural network modeling also shows that the accuracy of the optimizing differential method is better than the other two methods both in calibration set and prediction set. In general, the optimizing differential extraction method improves the data utilization and credibility compared with the existing extraction methods, and the modeling effect is better than the other two methods.

[1]  Ling Lin,et al.  Dynamic Spectrum for noninvasive blood component analysis and its advances , 2018, Applied Spectroscopy Reviews.

[2]  Gang Li,et al.  An efficient optimization method to improve the measuring accuracy of oxygen saturation by using triangular wave optical signal. , 2017, The Review of scientific instruments.

[3]  Ling Lin,et al.  Noninvasive hemoglobin measurement using dynamic spectrum. , 2017, The Review of scientific instruments.

[4]  Gang Li,et al.  New method of extracting information of arterial oxygen saturation based on ∑|𝚫|. , 2017, The Review of scientific instruments.

[5]  Ling Lin,et al.  Noninvasive hemoglobin measurement based on optimizing Dynamic Spectrum method , 2017 .

[6]  Gang Li,et al.  Calibration set selection method based on the “M + N” theory: application to non-invasive measurement by dynamic spectrum , 2016 .

[7]  Gang Li,et al.  [The Quality Assessment and Selection of Dynamic Spectrum Signal]. , 2016, Guang pu xue yu guang pu fen xi = Guang pu.

[8]  Ling Lin,et al.  Spectral data quality assessment based on variability analysis: application to noninvasive hemoglobin measurement by dynamic spectrum , 2015 .

[9]  Gang Li,et al.  [Application of EMD algorithm to the dynamic spectrum non-invasive measurement of hemoglobin]. , 2014, Guang pu xue yu guang pu fen xi = Guang pu.

[10]  Gang Li,et al.  [Compensation-fitting extraction of dynamic spectrum based on least squares method]. , 2014, Guang pu xue yu guang pu fen xi = Guang pu.

[11]  Gang Li,et al.  Double-sampling to improve signal-to-noise ratio (SNR) of dynamic spectrum (DS) in full spectral range , 2014 .

[12]  Ling Lin,et al.  [A spectrum extraction method based on uncertainty in noninvasive blood components examinaton]. , 2013, Guang pu xue yu guang pu fen xi = Guang pu.

[13]  Gang Li,et al.  Non-invasive measurement of haemoglobin based on dynamic spectrum method , 2013 .

[14]  Ling Lin,et al.  [D-value estimation of dynamic spectrum based on the statistical methods]. , 2012, Guang pu xue yu guang pu fen xi = Guang pu.

[15]  Ling Lin,et al.  [Single-trial estimation of dynamic spectrum]. , 2011, Guang pu xue yu guang pu fen xi = Guang pu.

[16]  Gang Li,et al.  [Increasing the precision of the noninvasive blood components measurement based on DS method using harmonic waves]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[17]  Carlos Eduardo Ferrante do Amaral,et al.  Current development in non-invasive glucose monitoring. , 2008, Medical engineering & physics.

[18]  John W. McMurdy,et al.  Noninvasive optical, electrical, and acoustic methods of total hemoglobin determination. , 2008, Clinical chemistry.

[19]  M. Ogawa,et al.  A New Non-invasive Method for Measuring Blood Glucose Using Instantaneous Differential Near Infrared Spectrophotometry , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Ling Lin,et al.  [Discussion about the prediction accuracy for dynamic spectrum by partial FFT]. , 2006, Guang pu xue yu guang pu fen xi = Guang pu.

[21]  Yukio Yamada,et al.  Noninvasive blood glucose assay using a newly developed near-infrared system , 2003 .

[22]  Ling Lin,et al.  [Non-invasive measurement of human hemoglobin concentration by dynamic spectrum method]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.