{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T12:08:31Z","timestamp":1789560511823,"version":"build-2803163510"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62361013"],"award-info":[{"award-number":["62361013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["AD2302300"],"award-info":[{"award-number":["AD2302300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Science and Technology Project of Guangxi Guike","award":["62361013"],"award-info":[{"award-number":["62361013"]}]},{"name":"the Science and Technology Project of Guangxi Guike","award":["AD2302300"],"award-info":[{"award-number":["AD2302300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a18020075","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T12:18:56Z","timestamp":1738585136000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals"],"prefix":"10.3390","volume":"18","author":[{"given":"Lei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1749-5816","authenticated-orcid":false,"given":"Yan","family":"Zhuang","sequence":"additional","affiliation":[{"name":"National Supercomputing Center in Xi\u2019an, Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8337-6147","authenticated-orcid":false,"given":"Yongbo","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Metabolic Reprogramming and Intelligent Medical Engineering for Chronic Diseases, Guilin 541004, China"},{"name":"Guangxi Center for Applied Mathematics, Guilin University of Electronic Science and Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alwabari, M., Alhamad, F., Alsahaf, F., Al Amer, F., Alniniya, F., Alherz, I., Omer, N., Bushehab, A., and Yassen, K. (2023). Can non-invasive spectrophotometric haemoglobin replace laboratory haemoglobin concentrations for preoperative anemia screening? A diagnostic test accuracy study. J. Clin. Med., 12.","DOI":"10.3390\/jcm12175733"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/08998280.2006.11928171","article-title":"Structure-function relations of human haemoglobins","volume":"Volume 19","year":"2006","journal-title":"Baylor University Medical Center Proceedings"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2TR02","DOI":"10.1088\/1361-6579\/ac41b7","article-title":"Progressive evaluation in spectroscopic sensors for non-invasive blood haemoglobin analysis\u2014A review","volume":"43","author":"Kumar","year":"2022","journal-title":"Physiol. Meas."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Appiahene, P., Asare, J.W., Donkoh, E.T., Dimauro, G., and Maglietta, R. (2023). Detection of iron deficiency anemia by medical images: A comparative study of machine learning algorithms. BioData Min., 16.","DOI":"10.1186\/s13040-023-00319-z"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1049\/ipr2.12930","article-title":"A self-supervised causal feature reinforcement learning method for non-invasive haemoglobin prediction","volume":"18","author":"Xu","year":"2024","journal-title":"IET Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"55003","DOI":"10.1088\/1361-6579\/accb3a","article-title":"Non-invasive detection of the content of white blood cells in the blood of humans based on dynamic spectrum","volume":"44","author":"Huo","year":"2023","journal-title":"Physiol. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6733","DOI":"10.1007\/s00216-023-04950-x","article-title":"High-precision non-invasive RBC and HGB detection system based on spectral analysis","volume":"415","author":"Wang","year":"2023","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bardadin, I., Petrov, V., Denisenko, G., Armaganov, A., Rubekina, A., Kopytina, D., Panov, V., Shatalov, P., Khoronenko, V., and Shegai, P. (2024). Non-invasive haemoglobin assessment with NIR imaging of blood vessels in transmittance geometry: Monte Carlo and experimental evaluation. Photonics, 11.","DOI":"10.3390\/photonics11010049"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ahsan, G.M., Gani, M.O., Hasan, M.K., Ahamed, S.I., Chu, W., Adibuzzaman, M., and Field, J. (2017, January 4\u20138). A novel real-time non-invasive haemoglobin level detection using video images from a smartphone camera. Proceedings of the IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), Turin, Italy.","DOI":"10.1109\/COMPSAC.2017.29"},{"key":"ref_10","first-page":"13","article-title":"Non-invasive haemoglobin monitoring device using K-Nearest neighbor and artificial neural network backpropagation algorithms","volume":"68","author":"Munadi","year":"2022","journal-title":"Int. J. Electron. Telecommun."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pinto, C., Parab, J., and Naik, G. (2020). Non-invasive haemoglobin measurement using embedded platform. Sens. Bio-Sens. Res., 9.","DOI":"10.1016\/j.sbsr.2020.100370"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105007","DOI":"10.1088\/1361-6579\/ad8299","article-title":"The influence of heart rate on the relationship between pulse transit time and systolic blood pressure","volume":"45","author":"Fu","year":"2024","journal-title":"Physiol. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4313","DOI":"10.1364\/BOE.7.004313","article-title":"Multi-wavelength photoplethysmography method for skin arterial pulse extraction","volume":"7","author":"Liu","year":"2016","journal-title":"Bio-Med. Opt. Express."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.asoc.2015.04.008","article-title":"Non-invasive prediction of haemoglobin level using machine learning techniques with the PPG signal\u2019s characteristics features","volume":"37","author":"Polat","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1109\/JBHI.2019.2954553","article-title":"Non-invasive estimation of haemoglobin using a multi-model stacking regressor","volume":"24","author":"Acharya","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","first-page":"3034260","article-title":"Development and validation of a photoplethysmography system for noninvasive monitoring of Haemoglobin concentration","volume":"2020","author":"Liu","year":"2020","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"79636","DOI":"10.1109\/ACCESS.2023.3300293","article-title":"Noninvasive Haemoglobin Measurements with Photoplethysmography in Wrist","volume":"11","author":"Lychagov","year":"2023","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e35777","DOI":"10.1016\/j.heliyon.2024.e35777","article-title":"Non-invasive haemoglobin measurement using an optical method","volume":"10","author":"Abiramee","year":"2024","journal-title":"Heliyon"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, S.H., Chuang, Y.C., and Chang, C.C. (2020). Development of a portable all-wavelength PPG sensing device for robust adaptive-depth measurement: A spectrometer approach with a hydrostatic measurement example. Sensors, 20.","DOI":"10.3390\/s20226556"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1038\/35065725","article-title":"Exploring complex networks","volume":"410","author":"Strogatz","year":"2001","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.measurement.2018.01.024","article-title":"Complex networks approach for depth of anesthesia assessment","volume":"119","author":"Diykh","year":"2018","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1109\/JBHI.2021.3128383","article-title":"Cuff-less blood pressure estimation from photoplethysmography via visibility graph and transfer learning","volume":"26","author":"Wang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"87077","DOI":"10.1109\/ACCESS.2024.3416444","article-title":"A visibility graph approach for multi-stage classification of parkinson\u2019s disease using multimodal data","volume":"12","author":"Raj","year":"2024","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, Z., Qin, H., Ge, W., Li, S., and Liang, Y. (2023). Research on a non-invasive haemoglobin measurement system based on four-wavelength photoplethysmography. Electronics, 12.","DOI":"10.3390\/electronics12061346"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.physa.2014.07.002","article-title":"From the time series to the complex networks: The parametric natural visibility graph. Physica A: Statistical Mechanics and its Applications","volume":"414","author":"Bezsudnov","year":"2014","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kong, T., Shao, J., Hu, J., Yang, X., Yang, S., and Malekian, R. (2021). EEG-based emotion recognition using an improved weighted horizontal visibility graph. Sensors, 21.","DOI":"10.3390\/s21051870"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1016\/j.physleta.2017.06.018","article-title":"Label propagation algorithm for community detection based on node importance and label influence","volume":"381","author":"Zhang","year":"2017","journal-title":"Phys. Lett. A"},{"key":"ref_28","first-page":"1","article-title":"The effects of external pressure on multi-wavelength photoplethysmography signal","volume":"48","author":"Panula","year":"2021","journal-title":"Comput. Cardiol. (CinC)"},{"key":"ref_29","first-page":"265","article-title":"Evaluation of eye-blinking dynamics in human emotion recognition using weighted visibility graph","volume":"11","author":"Goshvarpour","year":"2024","journal-title":"Front. Biomed. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1109\/JBHI.2024.3351867","article-title":"Multi-view cross-fusion transformer based on kinetic features for non-invasive blood glucose measurement using PPG signal","volume":"28","author":"Chen","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","unstructured":"Hoang, T.M., Vu, D.D., and Hoang, M.H. (August, January 31). Classification of electroencephalography signals with auditory stimuli using complex network. Proceedings of the Tenth International Conference on Communications and Electronics (ICCE), Danang, Vietnam."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Warren, K.M., Harvey, J.R., Chon, K.H., and Mendelson, Y. (2016). Improving pulse rate measurements during random motion using a wearable multichannel reflectance photoplethysmograph. Sensors, 16.","DOI":"10.3390\/s16030342"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"718","DOI":"10.14778\/1687627.1687709","article-title":"Graph clustering based on structural\/attribute similarities","volume":"2","author":"Zhou","year":"2009","journal-title":"Proc. VLDB Endow."},{"key":"ref_34","unstructured":"Narayan, S.S., Arif, I., Shalu, H., and Kadiwala, J. (2020). A smartphone-based multi-input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Turja, M.S., Kwon, T.H., Kim, H., and Kim, K.D. (2024, January 6\u20138). Noninvasive in-vivo estimation of glycated haemoglobin using digital volume pulse signal based on modified Beer-Lambert law. Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE59016.2024.10444367"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"112790","DOI":"10.1016\/j.eswa.2019.07.007","article-title":"Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm","volume":"138","author":"Abdulla","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"12089","DOI":"10.1088\/1742-6596\/1432\/1\/012089","article-title":"Non-invasive Haemoglobin measurement among pregnant women using photoplethysmography and machine learning","volume":"1432","author":"Lakshmi","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_38","first-page":"15318","article-title":"A novel noninvasive haemoglobin sensing device for anemia screening","volume":"21","author":"Kumar","year":"2021","journal-title":"Inst. Electr. Electron. Eng. (IEEE) Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, X., Meng, L., Su, H., Lv, B., Lv, C., Xie, G., and Chen, Y. (2022). Deep-learning-based haemoglobin concentration prediction and anemia screening using ultra-widefield fundus images. Front. Cell Dev. Biol., 10.","DOI":"10.3389\/fcell.2022.888268"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lychagov, V., Semenov, V., Volkova, E., Chernakov, D., Ahn, J., and Kim, J.Y. (2023, January 24\u201327). Non-invasive haemoglobin concentration measurements with multi-wavelength reflectance mode PPG sensor and CNN data processing. Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia.","DOI":"10.1109\/EMBC40787.2023.10341173"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/2\/75\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:25:23Z","timestamp":1760027123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/2\/75"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,1]]},"references-count":40,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["a18020075"],"URL":"https:\/\/doi.org\/10.3390\/a18020075","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,1]]}}}