  This study investigates artifact detection in clinical photoplethysmogram
signals using Transformer-based models. Recent findings have shown that in
detecting artifacts from the Pediatric Critical Care Unit at CHU Sainte-Justine
(CHUSJ), semi-supervised learning label propagation and conventional supervised
machine learning (K-nearest neighbors) outperform the Transformer-based
attention mechanism, particularly in limited data scenarios. However, these
methods exhibit sensitivity to data volume and limited improvement with
increased data availability. We propose the GRN-Transformer, an innovative
model that integrates the Gated Residual Network (GRN) into the Transformer
architecture to overcome these limitations. The GRN-Transformer demonstrates
superior performance, achieving remarkable metrics of 98% accuracy, 90%
precision, 97% recall, and 93% F1 score, clearly surpassing the Transformer's
results of 95% accuracy, 85% precision, 86% recall, and 85% F1 score. By
integrating the GRN, which excels at feature extraction, with the Transformer's
attention mechanism, the proposed GRN-Transformer overcomes the limitations of
previous methods. It achieves smoother training and validation loss,
effectively mitigating overfitting and demonstrating enhanced performance in
small datasets with imbalanced classes. The GRN-Transformer's potential impact
on artifact detection can significantly improve the reliability and accuracy of
the clinical decision support system at CHUSJ, ultimately leading to improved
patient outcomes and safety. Remarkably, the proposed model stands as the
pioneer in its domain, being the first of its kind to detect artifacts from PPG
signals. Further research could explore its applicability to other medical
domains and datasets with similar constraints.
