title: Automated unsupervised respiratory event analysis.
abstract: We recently presented a comprehensive automated off-line method for supervised respiratory event classification from uncalibrated respiratory inductive plethysmography signals. This method required training with a sample of clinical measurements classified by an expert. This human intervention is labor intensive and involves subjective judgments that may introduce bias to the automated classification. To address this we developed a novel method for unsupervised respiratory event classification, named AUREA (Automated Unsupervised Respiratory Event Analysis). This paper describes the algorithm underlying AUREA and demonstrates its successful application to respiratory signals acquired from infants in the postoperative recovery room. The advantages of AUREA are: first, it provides real-time classification of respiratory events; second, it requires no human intervention; and lastly, it has substantially better performance than the supervised method.
authors.affiliation: Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada. carlos.roblesrubio@mail.mcgill.ca
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Robles-Rubio
authors.forename: Karen A
authors.initials: KA
authors.lastname: Brown
authors.forename: Robert E
authors.initials: RE
authors.lastname: Kearney
mesh_terms: D000465:Algorithms; D001331:Automation; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D012119:Respiration