Hemoglobin concentration prediction regression model training method, application method and training system

Abstract

The invention provides a hemoglobin concentration prediction regression model training and application method, and system, and the method comprises the steps of obtaining a multi-channel PPG signal, and carrying out the signal processing of the multi-channel PPG signal; performing manual and automatic feature information extraction on the acquired multi-channel PPG signals, and fusing the manual feature information and the automatic feature information through a feature information fusion mechanism to realize accurate extraction of the feature information; establishing a hemoglobin concentration prediction regression model integrated based on a multi-machine learning algorithm for noninvasive detection, and obtaining a more stable detection result by utilizing efficient integration of a plurality of regression models and multiple regression predictions. According to the method, under the condition that the model training speed is guaranteed, the detection precision and generalization ability of the model can be improved, the method has the advantages of a non-invasive detection method and an invasive detection method, and the technical problem that the hemoglobin concentration of the human body cannot be continuously detected in real time in the prior art is solved.

Description

technical field

[0001] The invention relates to the technical field of non-invasive detection, in particular to a training and application method and system of a hemoglobin concentration prediction regression model. Background technique

[0002] At present, the methods for detecting hemoglobin concentration can be divided into two types: invasive detection and non-invasive detection. Invasive hemoglobin concentration detection methods include cyanmethemoglobin detection method and instant detection method, both of which need to take blood samples from the subject first, and then analyze the hemoglobin concentration value by a blood cell analyzer. Although the accuracy of the results obtained by this type of detection method is high, there are some shortcomings: the detection time cannot achieve continuous and real-time results, and the situation of critically ill patients cannot be understood in time; it is easy to cause wound infection, and it may also cause the subject to ...

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