Key result
A PPG-based classification-regression framework achieves ~93% accuracy for non-invasive haemoglobin measurement.
Why the study?
Non-invasive haemoglobin testing devices enable large-scale screening, but their accuracy is not comparable to traditional blood tests.
Does a classification-regression prediction framework using visibility graphs and network clustering improve the accuracy of non-invasive haemoglobin detection from PPG signals in volunteers?
Does a classification-regression prediction framework using visibility graphs and network clustering improve the accuracy of non-invasive haemoglobin detection from PPG signals in volunteers?
Effect estimate: MAE 6.67 g/L, RMSE 8.21 g/L, R2 0.64
The use of complex network technology and a classification-regression prediction framework significantly improves the accuracy of non-invasive haemoglobin detection using PPG signals.
Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a classification-regression prediction framework for non-invasive testing of haemoglobin using visibility graphs (VG) with network clustering of multi-sample pulse-wave-weighted undirected graphs as the features to optimize the detection accuracy of non-invasive haemoglobin measurements. Different prediction methods were compared by analyzing 608 segments of multiwavelength fingertip PPG signal data from 152 volunteers and analyzing and comparing the data and methods. The results showed that the classification using NVG with complex network clustering as features in the interval classification model was the best, with its classification accuracy (acc) of 93.35% and model accuracy of 88.28%. Among the regression models, the classification regression stack: SVM-Light Gradient Boosting Machine (LGBM) was the most effective, with a Mean Absolute Error (MAE) of 6.67 g/L, a Root Mean Square Error (RMSE) of 8.21 g/L, and an R-Square (R2) of 0.64. The results of this study indicate that the use of complex network technology in non-invasive haemoglobin detection can effectively improve its accuracy, and the detector designed in this study is promising to carry out a more accurate large-scale haemoglobin screening.
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Liu et al. (2025) studied Haemoglobin screening (n=152). Classification-regression prediction framework using visibility graphs and network clustering vs. Other prediction methods was evaluated on Classification accuracy and regression error for non-invasive haemoglobin measurements (MAE 6.67 g/L, RMSE 8.21 g/L, R2 0.64). A classification-regression framework using visibility graphs and network clustering of PPG signals achieved 93.35% classification accuracy and a mean absolute error of 6.67 g/L for haemoglobin.
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