A Novel Cuffless Blood Pressure Prediction: Uncovering New Features and New Hybrid ML Models

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), R-2, mean square error (MSE), and the mean absolute error (MAE). The obtained RMSE was 4.277 for systolic blood pressure (SBP) values using the Matern 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.

Automated summary

This paper investigates new methods for predicting cuffless blood pressure using PPG signals. The new feature extraction method involves extracting meaningful features from the PPG signals to predict systolic and diastolic blood pressure values. Regression models were used to predict cuffless blood pressure and their performances were evaluated using different metrics. The results show that the proposed feature extraction and regression models can predict cuffless blood pressure with reasonable accuracy and provide a novel approach for future development.