Abstract

Info

Title:: Non-invasive detection model for hemoglobin concentration based on support vector regression

Author(s):: PENG Fulai1; SHUI Yuanyuan2; ZHANG Ningling1; CHEN Cai1; WANG Weidong3; 1. Shandong Zhongke Advanced Technology Co., Ltd., Jinan 250000, China 2. School of Mathematics, Shandong University, Jinan 250100, China 3. Medical Device R&D and Clinical Evaluation Center of Chinese Peoples Liberation Army General Hospital, Beijing 100853, China

Keywords:: Keywords: hemoglobin concentration non-invasive detection photoplethysmography support vector regression

Abstract:: Abstract: 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.