Use of respiratory signal analysis to assess severity of Brachycephalic Obstructive Airway Syndrome (BOAS) in dogs
Journal article, 2024

Brachycephalic Obstructive Airway Syndrome (BOAS) is a potentially life-threatening condition that can be challenging to diagnose and grade objectively. The aim of this study was to investigate the use of respiratory signal analysis to assess severity of BOAS in dogs. Hundred and seventeen client-owned dogs of brachycephalic and non-brachycephalic breeds were enrolled. Respiratory sounds were recorded using an electronic stethoscope before and after a 3-minute exercise test (ET). Dogs were assigned a BOAS severity grade (BOAS 0–3) using a validated respiratory functional grading scheme. Signal analysis techniques were used to identify seven sound variables. Analysis of variance (ANOVA) was used to investigate associations between variables and BOAS severity and receiver operating characteristic (ROC) curves to assess the diagnostic efficacy of each sound variable. For each sound variable, there was a significant association with BOAS grade. An increase in BOAS grade resulted in greater sound magnitude in the frequency spectrum (0–1000 Hz), and in a greater contribution of lower frequencies (170–260 Hz). The variable “Peak 1” had the best performance in predicting BOAS negative (BOAS 0 +1) versus BOAS positive dogs (BOAS 2 + 3) before the ET; area under the curve (AUC) = 76.6 % (95 % confidence interval 67.4–85.8 %), whereas the variable “Valley 1” had the highest predictive value after the ET; AUC = 87.8 % (95 % confidence interval 81.4–94.3 %). Respiratory signal analysis has good potential for assessing BOAS severity and could be valuable for clinicians in clinical decision processes and for breeders when selecting suitable breeding dogs.

Stridor

Signal processing

Stertor

Brachycephaly

Signal analysis

Author

M. Dimopoulou

Swedish University of Agricultural Sciences (SLU)

Henrik Peterson

Chalmers, Physics, Materials Physics

Olivia Stensöta

Chalmers, Physics, Materials Physics

Magnus Karlsteen

Chalmers, Physics, Materials Physics

I. Ljungvall

Swedish University of Agricultural Sciences (SLU)

J. Ryden

Swedish University of Agricultural Sciences (SLU)

E. Skiöldebrand

Swedish University of Agricultural Sciences (SLU)

Veterinary Journal

1090-0233 (ISSN) 15322971 (eISSN)

Vol. 308 106261

Subject Categories

Clinical Science

DOI

10.1016/j.tvjl.2024.106261

More information

Latest update

11/13/2024