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Non-invasive estimation of hemoglobin using machine learning algorithms from i-PPG and PPG signals
A S Kaviya Dharshini1, J B Jeeva2
1Department of Sensor and Biomedical Technology, School of Electronics and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
Scientific Reports
|July 1, 2026
Summary
This study compares non-invasive hemoglobin estimation using photoplethysmogram (PPG) signals from a device (t-PPG) and a smartphone (i-PPG). The XGBoost model achieved high accuracy, demonstrating the potential of these methods for clinical applications.
Area of Science:
- Biomedical Engineering
- Medical Devices
- Machine Learning in Healthcare
Background:
- Non-invasive hemoglobin estimation is crucial for diagnosing and monitoring anemia.
- Photoplethysmogram (PPG) signals offer a promising avenue for non-invasive physiological measurements.
- Existing methods for hemoglobin estimation often require invasive procedures or specialized equipment.
Purpose of the Study:
- To compare the accuracy of hemoglobin estimation using two types of PPG signals: t-PPG (device-based) and i-PPG (smartphone-based).
- To evaluate the performance of various machine learning algorithms for non-invasive hemoglobin estimation.
- To validate the findings against clinical laboratory tests.
Main Methods:
- Collected t-PPG and i-PPG signals from 21 volunteers (aged 20-54).
- Filtered and processed PPG signals to remove noise and extracted optical attenuation-based features.
- Utilized machine learning algorithms including decision trees, boosted trees, bootstrap, NTanH, and XGBoost for hemoglobin estimation.
- Compared estimated hemoglobin values with clinical laboratory results.
Main Results:
- The XGBoost model demonstrated superior performance in estimating hemoglobin levels from both t-PPG and i-PPG signals.
- High R-squared values (0.9862 for t-PPG, 0.9812 for i-PPG) and low RMSE (0.0489 g/dL for t-PPG, 0.0329 g/dL for i-PPG) were achieved.
- Both t-PPG and i-PPG signals, when processed with machine learning, showed strong correlation with clinical laboratory measurements.
Conclusions:
- Non-invasive hemoglobin estimation using PPG signals, particularly with the XGBoost model, is accurate and reliable.
- Smartphone-based i-PPG offers a convenient and accessible alternative for hemoglobin estimation.
- These findings support the development of portable, non-invasive tools for hemoglobin monitoring in diverse healthcare settings.
