An Automated Algorithm to Extract Time Plane Features from the PPG Signal and its Derivatives for Personal Health Monitoring Application

Abstract

Recently, photoplethysmogram (PPG) signal is widely adopted in health monitoring devices for automated assessment of different cardiovascular parameters. However, research in the area of computerized health analysis using PPG signal features is still lagging behind. In this paper, a robust, automated yet simple algorithm is proposed for accurate detection of characteristic points from the PPG signal and its derivatives. The methodology follows amplitude thresholding, slope-reversal, and an empirical formula based approach. Finally, Baseline modulation is removed from the PPG dataset and features are extracted from the amplitude normalized PPG signal and its derivatives. Performance of the proposed algorithm is evaluated over MIMIC database as well as over real PPG data acquired from both healthy volunteers and cardiac patients. The algorithm exhibits high efficiency for all detected fiducial points with an average sensitivity, positive predictivity and detection accuracy of 99.80%, 99.84%, and 99.65%, respectively. Compared to the existing methods, the proposed algorithm offers complete characterization of the PPG signal and its derivatives.