Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices - 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 Antonio (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
Photoplethysmography (PPG) is a non-invasive technique widely used to monitor cardiovascular parameters such as heart rate. However, its reliability can be compromised by factors like motion artifacts. In this study, we extracted 27 statistical features from PPG signals and trained multiple machine learning models (XGBoost, CatBoost, Random Forest) to assess signal quality. Using a publicly available dataset of PPG time series, we evaluated model performance using sensitivity, positive predictive value, and F1-score. Our best model (CatBoost) achieved 94.7%, 95.9%, and 95.3% for these metrics, respectively. These results are comparable to state-of-the-art approaches but relying on relatively simple models.