Detection of squawks in respiratory sounds of mechanically ventilated COVID-19 patients
This provides health professionals with a new feature to assess potential deterioration in critically ill patients, though it is incremental as it applies existing detection methods to a specific medical context.
The researchers tackled the problem of detecting squawks (short inspiratory sounds) in respiratory sounds of mechanically ventilated COVID-19 patients, achieving an F1 score of 0.48 at the sound file level and 0.66 at the recording session level.
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.