Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography

Hemoglobin is an essential parameter in human blood. This paper proposes a non-invasive hemoglobin concentration measurement method based on the characteristic parameters of four-wavelength photoplethysmography (PPG) signals combined with machine learning. The DCM08 sensor and NRF52840 chip form a data acquisition system to collect 58 human fingertip photoelectric volumetric pulse wave signals. The 160 four-wavelength PPG signal feature parameters were constructed and extracted. The feature parameters were screened by combining three feature selection methods: reliefF, Chi-square score, and information gain. The top 10, 20, and 30 features screened were used as input to evaluate the prediction performance of different feature sets for hemoglobin. The prediction models used were XGBoost, support vector machines, and logistic regression. The results showed that the optimal performance of the 30 feature sets screened using the Chi-square test was achieved by the XGBoost model with a coefficient of determination (R2) of 0.997, root mean square error (RMSE) of 0.762 g/L, and mean absolute error (MAE) of 0.325 g/L. The study showed that the four-wavelength-based PPG signal feature parameters with the XGBoost algorithm could effectively achieve non-invasive detection of hemoglobin, providing a new measurement method in clinical practice.