A Robust PPG Time Plane Feature Extraction Algorithm for Health Monitoring Application

Life threatening consequences of chronic cardiovascular diseases (CVDs) have motivated the population around the world to opt for regular monitoring of vital signs using smart health monitoring devices. However, such easy-to-use, portable devices are found to be compatible with bio-signals that are easy-to-acquire. Hence, Photoplethysmogram (PPG) signal is popularly adopted in such devices as a competent alternative of existing standard bio-signals. But research in the area of computerized analysis of PPG signal to extract relevant clinical information is still insufficient. In this paper, a robust, automated yet simple feature extraction algorithm is proposed for the PPG signal through accurate detection of characteristic points. The methodology consists of signal derivative, amplitude thresholding slope-reversal and empirical formula based approach. Baseline modulation is removed from the PPG dataset and then features are extracted from the amplitude normalized PPG signal. Performance of the proposed algorithm is evaluated over the standard MIMIC database as well as over real PPG data acquired from both healthy volunteers and cardiac patients. The proposed algorithm offers high efficiency in terms of sensitivity, positive predictivity and low value of errors in the detected features.