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.

翻译：在这项工作中,我们设计了一种方法,通过制定呼吸周期估计算法、苏呼克候选病例识别法、地物提取法和集束法,在肺炎病人(如COVID-19病人)中可能出现呼吸错乱的呼吸道声音。最佳分类器在声音文件水平达到0.48F1,在记录会议水平达到0.66F1,这些初步结果很有希望,因为在吵闹的环境中,这些初步结果为卫生专业人员提供了评估重病患者潜在恶化的新特征。