{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T21:17:20Z","timestamp":1783631840854,"version":"3.55.0"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of Natural Science Foundation of Shandong Province","award":["ZR2020QF024"],"award-info":[{"award-number":["ZR2020QF024"]}]},{"name":"Project of Natural Science Foundation of Shandong Province","award":["ZR2021QH290"],"award-info":[{"award-number":["ZR2021QH290"]}]},{"name":"Project of Natural Science Foundation of Shandong Province","award":["ZR2021ZD40"],"award-info":[{"award-number":["ZR2021ZD40"]}]},{"name":"Project of Natural Science Foundation of Shandong Province","award":["2023CXPT039"],"award-info":[{"award-number":["2023CXPT039"]}]},{"name":"Project of Natural Science Foundation of Shandong Province","award":["YJZX202301"],"award-info":[{"award-number":["YJZX202301"]}]},{"name":"Taishan Industrial Experts Program, Major Basic Research Project of Shandong Natural Science Foundation","award":["ZR2020QF024"],"award-info":[{"award-number":["ZR2020QF024"]}]},{"name":"Taishan Industrial Experts Program, Major Basic Research Project of Shandong Natural Science Foundation","award":["ZR2021QH290"],"award-info":[{"award-number":["ZR2021QH290"]}]},{"name":"Taishan Industrial Experts Program, Major Basic Research Project of Shandong Natural Science Foundation","award":["ZR2021ZD40"],"award-info":[{"award-number":["ZR2021ZD40"]}]},{"name":"Taishan Industrial Experts Program, Major Basic Research Project of Shandong Natural Science Foundation","award":["2023CXPT039"],"award-info":[{"award-number":["2023CXPT039"]}]},{"name":"Taishan Industrial Experts Program, Major Basic Research Project of Shandong Natural Science Foundation","award":["YJZX202301"],"award-info":[{"award-number":["YJZX202301"]}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020QF024"],"award-info":[{"award-number":["ZR2020QF024"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021QH290"],"award-info":[{"award-number":["ZR2021QH290"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021ZD40"],"award-info":[{"award-number":["ZR2021ZD40"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["2023CXPT039"],"award-info":[{"award-number":["2023CXPT039"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["YJZX202301"],"award-info":[{"award-number":["YJZX202301"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences","award":["ZR2020QF024"],"award-info":[{"award-number":["ZR2020QF024"]}]},{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences","award":["ZR2021QH290"],"award-info":[{"award-number":["ZR2021QH290"]}]},{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences","award":["ZR2021ZD40"],"award-info":[{"award-number":["ZR2021ZD40"]}]},{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences","award":["2023CXPT039"],"award-info":[{"award-number":["2023CXPT039"]}]},{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences","award":["YJZX202301"],"award-info":[{"award-number":["YJZX202301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Non-invasive detection of hemoglobin (Hb) concentration is of great clinical value for health screening and intraoperative blood transfusion. However, the accuracy and stability of non-invasive detection still need to be improved to meet clinical requirement. This paper proposes a non-invasive Hb detection method using ensemble extreme learning machine (EELM) regression based on eight-wavelength PhotoPlethysmoGraphic (PPG) signals. Firstly, a mathematical model for non-invasive Hb detection based on the Beer-Lambert law is established. Secondly, the captured eight-channel PPG signals are denoised and fifty-six feature values are extracted according to the derived mathematical model. Thirdly, a recursive feature elimination (RFE) algorithm is used to select the features that contribute most to the Hb prediction. Finally, a regression model is built by integrating several independent ELM models to improve prediction stability and accuracy. Experiments conducted on 249 clinical data points (199 cases as the training dataset and 50 cases as the test dataset) evaluate the proposed method, achieving a root mean square error (RMSE) of 1.72 g\/dL and a Pearson correlation coefficient (PCC) of 0.76 (p &lt; 0.01) between predicted and reference values. The results demonstrate that the proposed non-invasive Hb detection method exhibits a strong correlation with traditional invasive methods, suggesting its potential for non-invasive detection of Hb concentration.<\/jats:p>","DOI":"10.3390\/s24061736","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:33:06Z","timestamp":1709811186000},"page":"1736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals"],"prefix":"10.3390","volume":"24","author":[{"given":"Fulai","family":"Peng","sequence":"first","affiliation":[{"name":"Medical Rehabilitation Research Center, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningling","family":"Zhang","sequence":"additional","affiliation":[{"name":"Medical Rehabilitation Research Center, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cai","family":"Chen","sequence":"additional","affiliation":[{"name":"Medical Rehabilitation Research Center, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengxia","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Shandong University, Jinan 250012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing 100853, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1182\/blood-2018-99-117261","article-title":"Low hemoglobin increases risk for stroke, kidney disease, elevated estimated pulmonary artery systolic pressure, and premature death in sickle cell disease: A systematic literature review and meta-analysis","volume":"132","author":"Ataga","year":"2018","journal-title":"Blood"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1080\/05704928.2018.1509344","article-title":"Dynamic Spectrum for noninvasive blood component analysis and its advances","volume":"54","author":"Wang","year":"2019","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15318","DOI":"10.1109\/JSEN.2021.3070971","article-title":"A Novel Noninvasive Hemoglobin Sensing Device for Anemia Screening","volume":"21","author":"Kumar","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_4","first-page":"535","article-title":"SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network","volume":"2018","author":"Hasan","year":"2018","journal-title":"AMIA Annu. Symp. Proc. AMIA Symp."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fan, Z., Zhou, Y., Zhai, H., Wang, Q., and He, H. (2022). A Smartphone-Based Biosensor for Non-Invasive Monitoring of Total Hemoglobin Concentration in Humans with High Accuracy. Biosensors, 12.","DOI":"10.3390\/bios12100781"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102477","DOI":"10.1016\/j.artmed.2022.102477","article-title":"An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset","volume":"136","author":"Dimauro","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"124634","DOI":"10.1016\/j.talanta.2023.124634","article-title":"Non-invasive detection of haemoglobin, platelets, and total bilirubin using hyperspectral cameras","volume":"260","author":"Li","year":"2023","journal-title":"Talanta"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Golap, M.A.-u., Raju, S.M.T.U., Haque, M.R., and Hashem, M.M.A. (2021). Hemoglobin and glucose level estimation from PPG characteristics features of fingertip video using MGGP-based model. Biomed. Signal Process. Control, 67.","DOI":"10.1016\/j.bspc.2021.102478"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103003","DOI":"10.1016\/j.infrared.2019.103003","article-title":"WSPXY combined with BP-ANN method for hemoglobin determination based on near-infrared spectroscopy","volume":"102","author":"Tian","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1109\/JBHI.2019.2954553","article-title":"Non-Invasive Estimation of Hemoglobin Using a Multi-Model Stacking Regressor","volume":"24","author":"Acharya","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12169","DOI":"10.1038\/s41598-021-91527-2","article-title":"Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform","volume":"11","author":"Hossain","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kwon, T.-H., and Kim, K.-D. (2022). Machine-Learning-Based Noninvasive In Vivo Estimation of HbA1c Using Photoplethysmography Signals. Sensors, 22.","DOI":"10.3390\/s22082963"},{"key":"ref_13","first-page":"3034260","article-title":"Development and Validation of a Photoplethysmography System for Noninvasive Monitoring of Hemoglobin Concentration","volume":"2020","author":"Liu","year":"2020","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, J., Sun, R., Liu, H., Wang, T., Cai, L., Chen, Z., and Heng, B. (2024). A Non-Invasive Hemoglobin Detection Device Based on Multispectral Photoplethysmography. Biosensors, 14.","DOI":"10.3390\/bios14010022"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Webster, J.G. (1997). Design of Pulse Oximeters, CRC Press.","DOI":"10.1887\/0750304677"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1088\/0967-3334\/36\/10\/2159","article-title":"A comb filter based signal processing method to effectively reduce motion artifacts from photoplethysmographic signals","volume":"36","author":"Peng","year":"2015","journal-title":"Physiol. Meas."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Afza, F., Sharif, M., Khan, M.A., Tariq, U., Yong, H.-S., and Cha, J. (2022). Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine. Sensors, 22.","DOI":"10.3390\/s22030799"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"911204","DOI":"10.3389\/fnhum.2022.911204","article-title":"Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals","volume":"16","author":"Peng","year":"2022","journal-title":"Front. Hum. Neurosci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gerke, O. (2020). Reporting Standards for a Bland-Altman Agreement Analysis: A Review of Methodological Reviews. Diagnostics, 10.","DOI":"10.3390\/diagnostics10050334"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1213\/ANE.0b013e318219a290","article-title":"Let\u2019s Think Clinically Instead of Mathematically About device accuracy","volume":"113","author":"Morey","year":"2011","journal-title":"Anesth. Analg."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e16806","DOI":"10.2196\/16806","article-title":"Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations","volume":"9","author":"Hasan","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1111\/anae.12681","article-title":"Improved non-invasive total haemoglobin measurements after in-vivo adjustment","volume":"69","author":"Miyashita","year":"2014","journal-title":"Anaesthesia"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"012050","DOI":"10.1088\/1742-6596\/1490\/1\/012050","article-title":"In Classification of thalassemia data using random forest algorithm","volume":"1490","author":"Aszhari","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1364\/BOE.5.001145","article-title":"Non-invasive prediction of hemoglobin levels by principal component and back propagation artificial neural network","volume":"5","author":"Ding","year":"2014","journal-title":"Biomed Opt Express."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/6\/1736\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:10:33Z","timestamp":1760105433000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/6\/1736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,7]]},"references-count":24,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24061736"],"URL":"https:\/\/doi.org\/10.3390\/s24061736","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,7]]}}}