Key result
A smart, automated heart health-monitoring device using a single photoplethysmography sensor and an OMP-based classifier was developed and its efficiency was proven across three different datasets.
A proposed smart automated heart health-monitoring device using a single PPG sensor and OMP-based classifier demonstrated efficiency across three datasets.
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Efficiency on datasets supports PPG monitoring exploration; leaves open prospective clinical validation before adoption.
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.
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Raj et al. (2020) studied Cardiac health. Smart automated heart health-monitoring (SAHM) device was evaluated on Algorithm efficiency and performance. A smart, automated heart health-monitoring device using a single photoplethysmography sensor and an OMP-based classifier was developed and its efficiency was proven across three different datasets.
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