Data from: COPD screening using time–frequency features of self-recorded respiratory sounds

Chronic obstructive pulmonary disease (COPD) is the third leading cause of
death worldwide, with up to 70% of cases remaining undiagnosed. This paper
proposes a COPD screening tool based on time-frequency representation
features of self-recorded respiratory sounds. Respiratory sound samples
(breath and cough sounds) were extracted from COPD and asymptomatic
non-COPD volunteers using a large, scientific-purpose database. We
analysed 39 time-frequency representation features of breath and cough
sounds, combined with age, sex, and smoking status, using Autoencoder
neural networks and random forest algorithms. We compared the performance
of different breath and cough random forest models built to detect COPD:
based exclusively on sound features, based exclusively on sociodemographic
characteristics, and based on sound features and sociodemographic
characteristics. Models including breathing features outperformed models
exclusively based on sociodemographic characteristics. Specifically, the
model combining sociodemographic characteristics and breathing features
achieved an AUC, accuracy, sensitivity, and specificity of 0.901, 0.836,
0.871, and 0.761, respectively, in the test set, representing a
substantial increase in AUC when compared to the model based exclusively
on sociodemographic characteristics (0.901 vs. 0.818). Our results suggest
that a lightweight collection of the time-frequency representation
features of self-recorded breathing sounds could effectively improve the
predictive performance of COPD screening or case-finding questionnaires.
COPD screening through self-recorded breathing sounds could be easily
integrated as a low-cost first step in case-finding programs, potentially
contributing to mitigate COPD underdiagnosis.