PPG Based Continuous Blood Pressure Monitoring Framework for Smart Home Environment

In this paper, we have proposed a computational framework for continuous blood pressure estimation using Photoplethysmogram (PPG) signal. The proposed framework is evaluated on the publicly available MIMIC Database. The database contains raw PPG data for different users and also the Arterial Blood Pressure (ABP) for calculating the systolic and diastolic blood pressure. Results showed that the Decision Tree Regressor boosted by Adaboost regressor could estimate systolic blood pressure with a mean average error of 2.07 and a standard deviation of 5.97 and the diastolic blood pressure with a mean average error of 1.15 and a standard deviation of 4.05. The results indicate that the proposed framework is an ideal candidate for integrating with the wearable devices for unobtrusive cuff less continuous blood pressure monitoring to provide real time update on the blood pressure of an individual with high degree of accuracy.