Development of an STM32-Based Noninvasive Hemoglobin Measurement System Using Multiwavelength Photoplethysmography

Continuous hemoglobin (Hb) monitoring can diagnose conditions such as polycythemia, anemia, and various cardiovascular diseases. Current Hb measurement techniques are still invasive, often causing discomfort and pain for the user. Noninvasive Hb measurement is a newer technology that is gaining popularity among researchers. This article proposes a new noninvasive Hb measurement approach using multiwavelength PPG signals. The transmissive mode PPG sensor and the reflective mode PPG sensor collected real-time PPG signals at four wavelengths: 560, 660, 880, and 940 nm. A novel set of 26 feature vectors, such as perfusion density (PD), PPG absorbance (ABR), molar extinction coefficient (MEC), magnitude of the prominent peak (PPM), frequency of prominent peak (PPF), and ABR ratio, are the first time computed in this work from the real-time acquired PPG signal. The performance of the proposed framework is analyzed by utilizing the optimized four different machine learning (ML) regression models such as the AdaBoost model, XGBoost model, BPNN model, and the CatBoost model. Furthermore, this work utilized Shapley additive explanation (SHAP) values to visualize the importance of each feature in Hb estimation, enabling a clear understanding of how different features contribute to the prediction process. This SHAP value helps to select the most relevant features for improving the accuracy and interpretability of the Hb estimation model. Results indicate that the BPNN model exhibited superior performance, yielding a mean absolute error (MAE) of 0.421, 0.624, and 1.57 g/dL for Dataset-1, Dataset-2, and in-house dataset, respectively. Additionally, the proposed framework is implemented on an embedded platform featuring an STM32F67ZI microcontroller, showcasing its effectiveness for real-time execution.