Non-Invasive Anemia Detection: A Multichannel PPG-Based Hemoglobin Estimation with Explainable Artificial Intelligence
Garima Sahu, Poorva Verma, Nachiket Tapas

TL;DR
This study introduces a non-invasive, explainable AI-based method using multichannel PPG signals to estimate hemoglobin levels and screen for anemia, aiming to replace invasive blood tests for large-scale and continuous monitoring.
Contribution
It develops a novel non-invasive framework combining multichannel PPG signals with explainable AI for hemoglobin estimation and anemia screening, enhancing interpretability and accuracy.
Findings
Achieved a mean absolute error of 8.50 g/L in hemoglobin estimation.
Demonstrated effective anemia screening using WHO thresholds.
Showed potential for non-invasive, continuous anemia monitoring.
Abstract
Anemia is a prevalent hematological disorder that requires frequent hemoglobin monitoring for early diagnosis and effective management. Conventional hemoglobin assessment relies on invasive blood sampling, limiting its suitability for large-scale or continuous screening. This paper presents a non-invasive framework for hemoglobin estimation and anemia screening using multichannel photoplethysmography (PPG) signals and explainable artificial intelligence. Four-wavelength PPG signals (660, 730, 850, and 940~nm) are processed to extract optical and cross-wavelength features, which are aggregated at the subject level to avoid data leakage. A gradient boosting regression model is employed to estimate hemoglobin concentration, followed by post-regression anemia screening using World Health Organization (WHO) thresholds. Model interpretability is achieved using SHapley Additive explanations…
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Taxonomy
TopicsNon-Invasive Vital Sign Monitoring · Biosensors and Analytical Detection · Advanced Chemical Sensor Technologies
