  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.
