Personalized Blood Pressure Estimation using Photoplethysmography and Wavelet Decomposition

Blood pressure (BP) is the most important indicator of cardiovascular diseases. Traditional cuff-based methods for measuring BP require manual intervention and time. These methods may lead to inaccurate measurements and are not practical for continuous BP monitoring, which is crucial for detecting abnormal BP fluctuations. In this study, we propose a personalized machine learning model to estimate BP using his/her previous BPs and the photoplethysmogram (PPG) signal, the simplest and most popular tool for non-invasive diagnosis. To best utilize the information contained in the PPG signal, we propose to apply wavelet decomposition to extract features from the PPG signal. The arterial blood pressure (ABP) time series is processed with an exponentially weighted moving average (EWMA) and a peak detection technique to derive the SBP, DBP, and their corresponding trends. Finally, a random forest model is used to construct a predictive model based on these features. The MIMIC dataset is used for analysis and comparison with other BP estimation methods. Our experimental results demonstrate the proposed approach has smaller estimation error than existing methods, with mean average errors (MAE) for SBP and DBP equal to 3.43 and 1.73, respectively.