Open Access

ARTICLE

Noninvasive Hemoglobin Estimation with Adaptive Lightweight Convolutional Neural Network Using Wearable PPG

Florentin Smarandache¹, Saleh I. Alzahrani², Sulaiman Al Amro³, Ijaz Ahmad⁴, Mubashir Ali^5,*

1 Mathematics, Physics, and Natural Science Division, The University of New Mexico, 705 Gurley Ave., Gallup, NM 87301, USA
2 Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, 34212, Saudi Arabia
3 Department of Computer Science, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
4 School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
5 Department of Software Engineering, Lahore Garrison University, Lahore, 54810, Pakistan

* Corresponding Author: Mubashir Ali. Email:

(This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)

Computer Modeling in Engineering & Sciences 2025, 144(3), 3715-3735. https://doi.org/10.32604/cmes.2025.068736

Received 05 June 2025; Accepted 01 September 2025; Issue published 30 September 2025

Abstract

Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body. Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes, where abnormal hemoglobin levels can indicate significant health issues. Traditional methods for hemoglobin measurement are invasive, causing pain, risk of infection, and are less convenient for frequent monitoring. PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure, sleep, blood glucose, and stress analysis. In this work, we propose a hemoglobin estimation method using an adaptive lightweight convolutional neural network (HMALCNN) from PPG. The HMALCNN is designed to capture both fine-grained local waveform characteristics and global contextual patterns, ensuring robust performance across acquisition settings. We validated our approach on two multi-regional datasets containing 152 and 68 subjects, respectively, employing a subject-independent 5-fold cross-validation strategy. The proposed method achieved root mean square errors (RMSE) of 0.90 and 1.20 g/dL for the two datasets, with strong Pearson correlations of 0.82 and 0.72. We conducted extensive post-hoc analyses to assess clinical utility and interpretability. A 1 g/dL clinical error tolerance evaluation revealed that 91.3% and 86.7% of predictions for the two datasets fell within the acceptable clinical range. Hemoglobin range-wise analysis demonstrated consistently high accuracy in the normal and low hemoglobin categories. Statistical significance testing using the Wilcoxon signed-rank test confirmed the stability of performance across validation folds ( > 0.05 for both RMSE and correlation). Furthermore, model interpretability was enhanced using Gradient-weighted Class Activation Mapping (Grad-CAM), supporting the model’s clinical trustworthiness. The proposed HMALCNN offers a computationally efficient, clinically interpretable, and generalizable framework for noninvasive hemoglobin monitoring, with strong potential for integration into wearable healthcare systems as a practical alternative to invasive measurement techniques.

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