Key result
The proposed automated algorithm for PPG signal feature extraction achieved an average sensitivity of 99.80%, positive predictivity of 99.84%, and detection accuracy of 99.65%.
Why the study?
Although photoplethysmogram signals are widely used in health monitoring devices for cardiovascular assessment, research in computerized health analysis using PPG signal features is still lagging behind.
The proposed automated algorithm accurately extracts time plane features from PPG signals, offering potential improvements for personal health monitoring devices.
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May enhance wearable PPG monitoring; leaves open clinical validation and outcome studies.
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.
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Chakraborty et al. (2019) studied Healthy volunteers and cardiac patients. Automated algorithm for PPG signal feature extraction vs. Existing methods was evaluated on Detection accuracy of characteristic points from the PPG signal and its derivatives. The proposed automated algorithm for PPG signal feature extraction achieved an average sensitivity of 99.80%, positive predictivity of 99.84%, and detection accuracy of 99.65%.
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