Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals

Photoplethysmogram (PPG) signals are widely used in healthcare for monitoring vital signs, but they are susceptible to motion artifacts that can lead to inaccurate interpretations. In this study, the use of label propagation techniques to propagate labels among PPG samples is explored, particularly in imbalanced class scenarios where clean PPG samples are significantly outnumbered by artifact-contaminated samples. With a precision of 91%, a recall of 90% and an F1 score of 90% for the class without artifacts, the results demonstrate its effectiveness in labeling a medical dataset, even when clean samples are rare. For the classification of artifacts our study compares supervised classifiers such as conventional classifiers and neural networks (MLP, Transformers, FCN) with the semi-supervised label propagation algorithm. With a precision of 89%, a recall of 95% and an F1 score of 92%, the KNN supervised model gives good results, but the semi-supervised algorithm performs better in detecting artifacts. The findings suggest that the semi-supervised algorithm label propagation hold promise for artifact detection in PPG signals, which can enhance the reliability of PPG-based health monitoring systems in real-world applications.