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PPG Embedded System For Blood Pressure Monitoring
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<br>Preventive healthcare requires steady monitoring of the blood strain (BP) of patients, which is not possible using conventional methods. Photoplethysmogram (PPG) indicators could be effectively used for this function as there is a physiological relation between the pulse width and BP and may be simply acquired using a wearable PPG sensor. However, growing real-time algorithms for wearable technology is a major challenge due to various conflicting requirements reminiscent of high accuracy, computationally constrained units, and restricted power provide. In this paper, we propose a novel characteristic set for continuous, actual-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), adopted by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these instances are calculated to kind a feature set that can be utilized for classification of PPG alerts into one among the 2 lessons: normal or abnormal BP. No public dataset is out there for such study and therefore a prototype is developed to gather PPG signals alongside BP measurements. The proposed characteristic set shows superb efficiency with an general accuracy of roughly 95%. Although the proposed function set is efficient, the significance of particular person options varies significantly (validated utilizing significance testing) which led us to perform weighted voting of options for classification by performing autoregressive modeling. Our experiments show that the only linear classifiers produce very good results indicating the energy of the proposed feature set. The weighted voting improves the outcomes considerably, producing an total accuracy of about 98%. 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