The relationship between the perfusion index and precision of noninvasive blood component measurement based on dynamic spectroscopy

Noninvasive blood component measurement is a relevant topic in biomedical engineering. Noninvasive blood component measurement based on dynamic spectroscopy can effectively avoid the influence of individual differences and improve its prediction precision. The relationship between the perfusion index and precision of hemoglobin is explored in this paper. The method “perfusion index grouping optimization modeling” is put forward to restrain the influence of the perfusion index and to improve the prediction precision of hemoglobin. The study is based on clinical trial data from 340 volunteers, and the age of volunteers ranges from 22 to 82. The extreme learning machine modeling approach is used in our study. By considering the influence of the perfusion index, the root mean square error of the prediction set has been improved from 12.8753 to 11.3039 and the determination coefficient of the prediction set has been improved from 0.4728 to 0.5107. The result indicates that the perfusion index has an effect on the precision of noninvasive blood component measurement based on dynamic spectroscopy. By using the “perfusion index grouping optimization modeling” method, the root mean square error of the prediction set has been improved to 10.6274 and the determination coefficient of the prediction set has been improved to 0.6931. The result means that this method can effectively restrain the influences of the perfusion index and improve the prediction precision of noninvasive blood component measurement based on dynamic spectroscopy. In addition, the interference factors of this method are discussed. The prediction precision of blood components can be further improved by controlling these factors.

[1]  Josef Högel,et al.  Decreased accuracy of pulse oximetry measurements during low perfusion caused by sepsis: is the perfusion index of any value? , 2006, Intensive Care Medicine.

[2]  H. Asada,et al.  Utility of the Photoplethysmogram in Circulatory Monitoring , 2008, Anesthesiology.

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

[4]  P A Kyriacou,et al.  Estimation of instantaneous venous blood saturation using the photoplethysmograph waveform. , 2015, Physiological measurement.

[5]  Judith R Mourant,et al.  Hemoglobin parameters from diffuse reflectance data , 2014, Journal of biomedical optics.

[6]  E. Chan,et al.  Pulse oximetry: understanding its basic principles facilitates appreciation of its limitations. , 2013, Respiratory medicine.

[7]  J. Cohn,et al.  Noninvasive pulse wave analysis for the early detection of vascular disease. , 1995, Hypertension.

[8]  K. Shelley.  Photoplethysmography: Beyond the Calculation of Arterial Oxygen Saturation and Heart Rate , 2007, Anesthesia and analgesia.

[9]  Xibo Feng,et al.  Fast switching thyristor applied in nanosecond-pulse high-voltage generator with closed transformer core. , 2013, The Review of scientific instruments.

[10]  Rammohan V. Maikala,et al.  Modified Beer's Law – historical perspectives and relevance in near-infrared monitoring of optical properties of human tissue , 2010 .

[11]  K Yamakoshi,et al.  Pulse glucometry: A new approach for noninvasive blood glucose measurement using instantaneous differential near-infrared spectrophotometry. , 2006, Journal of biomedical optics.

[12]  P. Kyriacou.  Pulse oximetry in the oesophagus , 2006, Physiological measurement.

[13]  Yu Sun,et al.  Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging , 2016, IEEE Transactions on Biomedical Engineering.

[14]  Han Zhao,et al.  Extreme learning machine: algorithm, theory and applications , 2013, Artificial Intelligence Review.

[15]  John Allen.  Photoplethysmography and its application in clinical physiological measurement , 2007, Physiological measurement.

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

[17]  Ling Lin,et al.  Influence of water on noninvasive hemoglobin measurement by Dynamic Spectrum , 2013 .

[18]  J. Sinex.  Pulse oximetry: principles and limitations. , 1999, The American journal of emergency medicine.

[19]  Elfed Lewis,et al.  Non-invasive measurement of blood components , 2011, 2011 Fifth International Conference on Sensing Technology.