Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

Abstract

A growing attention is given to exploiting Photoplethysmography (PPG) signals in non-invasively measuring of many physiological vital signs. Many machine deep learning models were trained for predicting the continuous arterial blood pressure (ABP) or just the systolic and diastolic blood pressure (BP) values based on a public database. However, jointly cleaning the PPG-ABP dataset that is the most critical pre-processing step for training quality is still in need for more investigations. There is a considerable amount of anomaly data that has to be excluded before any training stage. This paper introduces a two-level joint PPG-ABP cleaning technique conducted in a signal level and per-beat level. Many quality metrics have been checked successively for excluding improper data. These metrics achieve a coarse cleaning step. Finally, principal component analysis (PCA) is exploited for fine cleaning the remaining data from the former stage. The cleaning efficiency is evaluated by measuring its impact on the deep-learning-based BP estimation models. The trained model based on our cleaned data shows performance enhancement in terms of prediction error and the correlation between the predicted and ground-truth BP. Segmented and cleaned PPG/ABP dataset will be publicly available in both signal level and beat level. Based on the simulation results, the proposed cleaning technique enhances standard deviation of the prediction error of systolic and diastolic blood pressure by 11.68 % and 10.81 %, respectively. Also, it enhances mean absolute error of the prediction of systolic and diastolic blood pressure by 14.79 % and 11.70 %, respectively.

Keywords

• Photoplethysmography
• PPG-ABP dataset
• data cleaning
• PPG quality metrics
• PPG
• anomaly detection