Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals
IEEE Access(2023)
Abstract
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
of 90
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
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.
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Key words
Motion artifacts,Imbalanced classes,Label Propagation algorithm,Machine Learning classifiers,Photoplethysmogram (PPG) signals
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