A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment
Hemoglobin (Hb) concentration is a fundamental physiological marker widely used in the diagnosis of anemia and the assessment of cardiovascular health. Although invasive blood testing provides high accuracy, its reliance on laboratory infrastructure limits scalability and real-time applicability. Here, we present Hb-PPG, a four-wavelength photoplethysmography (PPG) dataset designed to support research on non-invasive hemoglobin assessment and cardiovascular monitoring. The dataset comprises 1008 PPG signal segments acquired at 660, 730, 850, and 940 nm from 252 adult subjects, alongside reference measurements of hemoglobin, fasting blood glucose, and brachial artery systolic and diastolic blood pressure. Hb-PPG enables systematic investigation of wavelength-dependent PPG signal characteristics and their relationships with hematological and hemodynamic parameters. By providing high-quality, multi-wavelength optical signals with clinically grounded reference data, this dataset facilitates the development, validation, and benchmarking of non-invasive approaches for hemoglobin estimation and related vascular health applications. The dataset is intended to support algorithm development, benchmarking, and methodological studies in non-invasive hemoglobin estimation, rather than direct clinical diagnosis.
Authors
- Lei Liu (ORCID: https://orcid.org/0000-0001-8333-4775)
- Guanfeng Huang
- Shiyong Li (ORCID: https://orcid.org/0000-0002-0229-9259)
- Chen LiangQiu
- Yongbo Liang
- Yan Zhuang (ORCID: https://orcid.org/0009-0009-1749-5816)
- Zhencheng Chen
- Mohamed Elgendi
Institutions
- Guangxi Medical University (CN)
- Khalifa University of Science and Technology (AE)
- National Supercomputing Center in Wuxi (CN)
- National Supercomputing Center of Tianjin (CN)
- Guilin University of Electronic Technology (CN)
Publication Details
- Journal
- Scientific Data
- Published
- 2026-03-02
- DOI
- https://doi.org/10.1038/s41597-026-06945-6
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Khalifa University of Science, Technology and Research