Automated Quality Control of Forced Oscillation Measurements: Respiratory Artifact Detection with Advanced Feature Extraction

The forced oscillation technique (FOT) can provide unique and clinically relevant lung function information with little cooperation with subjects. However, FOT has higher variability than spirometry, possibly because strategies for quality control and reducing artifacts in FOT measurements have yet to be standardized or validated. Many quality control procedures either rely on simple statistical filters or subjective evaluation by a human operator. In this study, we propose an automated artifact removal approach based on the resistance against flow profile, applied to complete breaths. We report results obtained from data recorded from children and adults with and without asthma. Our proposed method has 76% agreement with a human operator for the adult dataset and 79% for the paediatric dataset. Furthermore, we assessed the variability of respiratory resistance measured by FOT using within-session variation (wCV), between-session variation (bCV). In the asthmatic adults test dataset, our method was again similar to that of the manual operator for wCV (6.5 vs. 6.9%), and significantly improved bCV (8.2 vs. 8.9%). In the pediatric test dataset, the wCV of our proposed automated method yielded similar variability to the manual operator (8.2 compared to 8.6%). Our combined automated breath removal approach based on advanced feature extraction offers better or equivalent quality control of FOT measurements compared to an expert operator and computationally more intensive methods in terms of accuracy and reducing intra-subject variability.

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