# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
###### Abstract  
Effective management of diabetes requires accurate monitoring of blood glucose levels. Traditional invasive methods for such monitoring can be cumbersome and uncomfortable for patients. In this study, we introduce a noninvasive approach to estimate blood glucose levels using photoplethysmography (PPG) signals. We have focused on blood glucose prediction using wrist PPG signals and explored various PPG waveform-based features, including AC to DC ratio (AC/DC) and intrinsic mode function (IMF)-based features derived from empirical mode decomposition (EMD). To the best of our knowledge, no studies have been found using EMD-based features to estimate blood glucose levels noninvasively. Additionally, feature importance-based selection has also been used to further improve the accuracy of the proposed model. Among the four machine learning algorithms considered in this study, CatBoost consistently outperformed XGBoost, LightGBM, and random forest across a wide number of features. The best performing model, CatBoost, achieved Pearson's r of 0.96, MSE 0.08, R\({}^{2}\) score 0.92, and MAE 8.01 when considering the top 50 features selected from both PPG waveform-based features and IMF-based features. The \(p\)-values for all models were \(<\)0.001, indicating statistically significant correlations. Overall, this study provides valuable insights into the feasibility and effectiveness of noninvasive blood glucose monitoring using advanced machine learning techniques. Keywords:photoplethysmography; blood glucose; diabetes; machine learning; empirical mode decomposition +
Footnote †: journal: Medical  
circulatory system. This study utilizes a wrist-worn PPG sensor that employs a reflective approach for signal detection. The radial arteries at the wrist, which is wider than the fingertip capillaries, provide better signal quality in reflective mode compared to the transmissive mode typically used at the fingertip [3]. Furthermore, the wrist serves as a more convenient location for data collection with wearable PPG devices compared to other potential sites such as the finger, earlobe [4]. Recent studies have shown varying degrees of success applying machine learning techniques to PPG data to estimate blood glucose levels. In our previous study [5; 6; 7], both transmissive and reflective PPG signals have been used for noninvasive HbA1c estimation. In this study, we focused on noninvasively estimating blood glucose levels by using wrist PPG signals and applying empirical mode decomposition (EMD)-based machine learning algorithms. Algorithms such as random forest (RF), XGBoost, CatBoost, and LightGBM [8; 9; 10; 11] have been applied to prediction models with the goal of achieving accurate glucose level estimation from PPG signals and diabetes datasets. However, some PPG-based estimations include external features such as BMI, SpO\({}_{2}\), age, and other relevant factors to improve the accuracy of predictions. Although the application of machine learning algorithms for blood glucose prediction using PPG signals has yielded promising outcomes, there is still ample room for research, especially in the exploration and application of typical PPG-based features. This issue must be addressed to increase the accuracy and reliability of the approach. Previous studies have mainly applied EMD to PPG signals to estimate heart rate [12], respiratory rate [12; 13]. However, this proposed work leverages EMD to identify key features of blood glucose estimation and expand its application scope beyond traditional vital signs monitoring. In contrast to traditional invasive and minimally invasive methods, PPG technology presents a more patient-friendly solution for blood glucose monitoring. Current methods, such as finger-prick testing and microneedle implantation, often cause discomfort, adherence issues and are subject to delays in glucose readings. PPG overcomes many of these limitations by being noninvasive and wearable.