A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals  
[PERSON]  
1 School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; [EMAIL_ADDRESS] (L.L.L. ; [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (X.Z.) [PERSON]  
1 School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; [EMAIL_ADDRESS] (L.L.L. ; [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (X.Z.) [PERSON]  
1 School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; [EMAIL_ADDRESS] (L.L.L. ; [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (X.Z.) [PERSON]  
2 National Supercomputing Center in Xi'an, Xi'an 70100, China; [EMAIL_ADDRESS] (Huangyi@162. 527 images of test subjects' palms to screen for anemia in 2023 and achieved classification results with 99.26% accuracy using a Bayesian classifier [4]. In 2024, [PERSON], [PERSON] et al. used eyelid images to predict hemoglobin concentration and proposed two networks, Actor Predictor and Hemoglobin Predictor, for predicting hemoglobin concentration. The results show that the prediction framework can achieve an MAE of 1.19 g/dL, representing a successful attempt for medical images to be used for haemoglobin regression prediction [5]. Among the non-invasive detection methods, spectral detection has also interested researchers [6]. Compared with image detection, this method has more interpretability for Hb, but its signal is more difficult to obtain. In recent studies, [PERSON], [PERSON], et al. used spectroscopic assay for erythrocyte and Hb detection, which developed a multi-wavelength spectroscopic acquisition device and used spectral data from 272 subjects. Its resultant accuracy in the RMSE of haemoglobin was reduced by 0.85 g/L compared to the classical spectral data, which was better [7]. [PERSON] made a more novel attempt to analyze multispectral signals using Monte Carlo analysis. A relative error of 15 g/L was obtained using a tissue model for Hb detection [8]. PPG is one of the non-invasive methods for Hb detection [9]. Hb non-invasive detectors, developed using PPG technology, can be integrated into wearable devices [10], achieving risk-free detection, convenient result acquisition, and easy operation [11], enabling the measurement of multiple valuable physiological parameters [12]. The detection principle is based on the Beer-Lambert law. Due to the different absorbance of different substances in the blood, the information in the PPG signal obtained is not the same. Some relevant studies have shown that deoxyHb and oxyhaemoglobin in the blood are very sensitive to the light intensity of 600 nm-1000 nm. Therefore, PPG signals for non-invasive detection of Hb feature detection methods using different wavelengths of light [13]. In 2015, [PERSON] et al. extracted 40 features related to PPG signaling from dual-wavelengths (660 nm, 905 nm). They used machine learning with neural network models to predict Hb levels after feature selection. The study used data from 20 individuals and obtained a small range of excellent results, with MSE = 0.0027 [14]. In 2020, [PERSON] et al. proposed a multi-model stacking framework for Hb prediction using PPG signals at four wavelengths (590 nm, 660 nm, 810 nm, and 940 nm), achieving a good RMSE = 1.353 g/dL [15]. In the same year, [PERSON] et al. distinguished anemia ranges using eight-wavelength PPG signals (610-940 nm) and obtained favorable outcomes [16]. In 2023, [PERSON] et al. used six wavelengths (655 nm, 765 nm, 805 nm, 850 nm, 940 nm, 975 nm) of reflective PPG signals for noninvasive haemoglobin detection, predicted them using machine learning and deep learning models, and compared the results. Among them, the best result was MAE = 12.66 g/L, R = 0.66, which is a more optimistic result [17]. By 2024, [PERSON] et al.