Detection of squawks in respiratory sounds of mechanically ventilated COVID-19 patients

TL;DR

This paper presents a new algorithm for detecting squawks in respiratory sounds of ventilated COVID-19 patients, aiding clinicians in monitoring patient deterioration despite noisy conditions.

Contribution

The study introduces a novel method combining respiratory cycle estimation, feature extraction, and clustering for squawk detection in ventilated patients.

Findings

F1 score of 0.48 at sound file level

F1 score of 0.66 at recording session level

Effective in noisy environments

Abstract

Mechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, squawk candidate identification, feature extraction, and clustering. The best classifier reached an F1 of 0.48 at the sound file level and an F1 of 0.66 at the recording session level. These preliminary results are promising, as they were obtained in noisy environments. This method will give health professionals a new feature to assess the potential deterioration of critically ill patients.