[en] This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenarios and limited data availability scenarios, where clean PPG samples are significantly outnumbered by artifact-contaminated samples. We investigated a dataset comprising PPG recordings from 1571 patients, wherein approximately 82% of the samples were identified as clean, while the remaining 18% were contaminated by artifacts. Our research compares the performance of supervised classifiers, such as conventional classifiers and neural networks (Multi-Layer Perceptron (MLP), Transformers, Fully Convolutional Network (FCN)), with the semi-supervised Label Propagation (LP) algorithm for artifact classification in PPG signals. The results indicate that the LP algorithm achieves a precision of 91%, a recall of 90%, and an F1 score of 90% for the 'artifacts' class, showcasing its effectiveness in annotating a medical dataset, even in cases where clean samples are rare. Although the K-Nearest Neighbors (KNN) supervised model demonstrated good results with a precision of 89%, a recall of 95%, and an F1 score of 92%, the semi-supervised algorithm excels in artifact detection. In the case of imbalanced and limited pediatric intensive care environment data, the semi-supervised LP algorithm is promising for artifact detection in PPG signals. The results of this study are important for improving the accuracy of PPG-based health monitoring, particularly in situations in which motion artifacts pose challenges to data interpretation.
Disciplines :
Computer science
Author, co-author :
Macabiau, Clara ✱; Biomedical Information Processing Laboratory, École de Technologie Supérieure, Montréal, Canada
LE, Thanh-Dung ✱; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Biomedical Information Processing Laboratory, École de Technologie Supérieure, Montréal, Canada
Albert, Kevin ✱; Université de Montréal, CHU Sainte-Justine Research Center, CHU Sainte-Justine Hospital, Montréal, Canada
Shahriari, Mana ✱; Université de Montréal, CHU Sainte-Justine Research Center, CHU Sainte-Justine Hospital, Montréal, Canada
Jouvet, Philippe ✱; Université de Montréal, CHU Sainte-Justine Research Center, CHU Sainte-Justine Hospital, Montréal, Canada
Noumeir, Rita ✱; Biomedical Information Processing Laboratory, École de Technologie Supérieure, Montréal, Canada
✱ These authors have contributed equally to this work.
External co-authors :
no
Language :
English
Title :
Label Propagation Techniques for Artifact Detection in Imbalanced Classes Using Photoplethysmogram Signals
Publication date :
2024
Journal title :
IEEE Access
ISSN :
2169-3536
Publisher :
Institute of Electrical and Electronics Engineers Inc.
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