This paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices. The new feature extraction method involves extracting meaningful features (time and chaotic features) from the PPG signals in the prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. These extracted features are then used as inputs to regression models, which are used to predict cuffless blood pressure. The regression model performances were evaluated using root mean squared error (RMSE), R², mean square error (MSE), and the mean absolute error (MAE). The obtained RMSE was 4.277 for systolic blood pressure (SBP) values using the Matérn 5/2 Gaussian process regression model. The obtained RMSE was 2.303 for diastolic blood pressure (DBP) values using the rational quadratic Gaussian process regression model. The results of this study have shown that the proposed feature extraction and regression models can predict cuffless blood pressure with reasonable accuracy. This study provides a novel approach for predicting cuffless blood pressure and can be used to develop more accurate models in the future.

Estimating blood sugar levels is a critical task in effective diabetes management. This study focuses on leveraging the power of machine learning models such as CatBoost, XGBoost, and Extra Trees Regressor, along with explainable AI techniques like SHAP values and confusion matrices, to predict blood sugar levels using Photoplethysmography (PPG) signals. The dataset used in this research is carefully selected for glucose prediction from PPG signals and consists of data from 217 individuals. Information for each individual includes laboratory glucose measurements and approximately one minute of recorded finger PPG signals. Among the various machine learning models tested, CatBoost emerged as the best-performing model in predicting blood sugar levels. The CatBoost model demonstrated its efficiency and accuracy in glucose level predictions by achieving an impressive coefficient of determination (R2) of 0.7191 and a mean absolute error (MAE) of 25.21. Feature importance analysis highlighted the significance of specific features like median deviation and kurtosis in the predictive model built with CatBoost, emphasizing their critical role in determining blood sugar levels. The inclusion of explainable AI techniques enhanced the interpretability and transparency of predictive models. In conclusion, this research underscores the potential of machine learning-based approaches in predicting blood sugar levels from PPG signals. By leveraging advanced models like CatBoost and utilizing explainable AI methods, this study paves the way for improved diabetes management through accurate, non-invasive, and data-driven predictive methodologies.