Chronic obstructive pulmonary disease screening using time-frequency features of self-recorded respiratory sounds.

ABSTRACT:

Objectives

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.

Materials and methods

Respiratory sound samples (breath and cough sounds) were extracted from COPD and asymptomatic non-COPD volunteers using a large, scientific-purpose database. We analyzed 39 time-frequency representation features of breath and cough sounds, combined with age, sex, and smoking status, using Autoencoder neural networks and random forest (RF) algorithms. We compared the performance of different breath and cough RF models built to detect COPD: one based exclusively on sound features, on

SUBMITTER: Tena A 

PROVIDER: S-EPMC12322310 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature