关键词:

Abstract: Objective　To study the application value of respiratory sound characteristic parameter analysis based on electronic stethoscope in pediatric pneumonia，and evaluate effectiveness of the zero-crossing rate and short-term energy parameters in identifying pulmonary crackles and wheezes. Methods　Utilizing an electronic stethoscope，we collected respiratory sounds from children hospitalized with pneumonia at The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University from July 2021 to December 2022，extracted four feature parameters—maximum zero-crossing rate（zcrMax），minimum zero-crossing rate（zcrMin），maximum short-term energy（enMax），and minimum short-term energy（enMin）-to construct a model for the identification of pulmonary crackles and wheezes，and assessed the model performance via the receiver operating characteristic（ROC） curve，accuracy，sensitivity，and specificity. Results　In this study，a cohort of 5，157 respiratory sounds data entries were included. The maximum zero-crossing rate in Group A（crackles only） was significantly higher than that in the other three groups（P<0.001）. The maximum short-term energy in Group B（wheezes only） was greater than that in both Group A and Group D（normal breath sounds）（P<0.001）. A significant difference in the minimum shortterm energy was observed between Group B and Group C（both crackles and wheezes）（P=0.004）. Random forest model demonstrated higher accuracy compared to linear discriminant analysis，support vector machine，and Logistic regression model. The random forest model achieved an accuracy of 68.5% in identifying the presence of abnormal breath sounds，and its accuracy reached 75.2% for identifying the co-existence of both crackles and wheezes. Conclusion　It is feasible to use the zero-crossing rate and short-term energy as characteristic parameters to distinguish pulmonary crackles and wheezes. Constructing the random forest model using these characteristic parameters of breath sounds based on electronic stethoscope demonstrates certain advantages in the diagnosis of childhood pneumonia and provides a new technical approach for auxiliary diagnosis.

Key words: