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Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices by Dias, Felipe M.; Toledo, Marcelo A. F.; Cardenas, Diego A. C.; Almeida, Douglas A.; Oliveira, Filipe A. C.; Ribeiro, Estela; Krieger, Jose E.; Gutierrez, Marco A. is a scholarly article available to read on EtoBox.

What is Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices about?

Photoplethysmography (PPG) is a non-invasive technology that measures changes in blood volume in the microvascular bed of tissue. It is commonly used in medical devices such as pulse oximeters and wrist worn heart rate monitors to monitor cardiovascular hemodynamics. PPG allows for the assessment of parameters (e.g., heart rate, pulse waveform, and peripheral perfusion) that can indicate conditions such as vasoconstriction or vasodilation, and provides information about microvascular blood flow, making it a valuable tool for monitoring cardiovascular health. However, PPG is subject to a number of sources of variations that can impact its accuracy and reliability, especially when using a wearable device for continuous monitoring, such as motion artifacts, skin pigmentation, and vasomotion. In this study, we extracted 27 statistical features from the PPG signal for training machine-learning models based on gradient boosting (XGBoost and CatBoost) and Random Forest (RF) algorithms to assess quality of PPG signals that were labeled as good or poor quality. We used the PPG time series from a publicly available dataset and evaluated the algorithm s performance using Sensitivity (Se), Pos

Author

Dias, Felipe M.; Toledo, Marcelo A. F.; Cardenas, Diego A. C.; Almeida, Douglas A.; Oliveira, Filipe A. C.; Ribeiro, Estela; Krieger, Jose E.; Gutierrez, Marco A.

Published

2023

Language

EN