Non-invasive detection model for hemoglobin concentration based on support vector regression
To achieve non-invasive detection of hemoglobin concentration,a hemoglobin concentration detection method based on support vector regression is designed.A mathematical model for non-invasive hemoglobin detection is established based on the Beer-Lambert law.After removing the noise and baseline drift from the collected photoplethysmography signals,hemoglobin concentration information is extracted,and a recursive feature elimination algorithm is used to select the extracted information and eliminate redundant features.Finally,29 key features are identified as input to construct a hemoglobin prediction model using support vector regression algorithm.Experimental validation is conducted on 249 clinical data samples(199 cases in training dataset and 50 in test dataset),resulting in a root mean square error of 1.83 g/dL between predicted values and references,with a correlation coefficient of 0.75(P<0.01),demonstrating the high consistency of the proposed method and traditional invasive detection methods.
万方数据
维普
