Smart automated heart health monitoring using photoplethysmography signal classification

This paper proposes a smart, automated heart health-monitoring (SAHM) device using a single photoplethysmography (PPG) sensor that can monitor cardiac health. The SAHM uses an Orthogonal Matching Pursuit (OMP)-based classifier along with low-rank motion artifact removal as a pre-processing stage. Major contributions of the proposed SAHM device over existing state-of-the-art technologies include these factors: (i) the detection algorithm works with robust features extracted from a single PPG sensor; (ii) the motion compensation algorithm for the PPG signal can make the device wearable; and (iii) the real-time analysis of PPG input and sharing through the Internet. The proposed low-cost, compact and user-friendly PPG device can also be prototyped easily. The SAHM system was tested on three different datasets, and detailed performance analysis was carried out to show and prove the efficiency of the proposed algorithm.