respiratory, lung, health abnormalities, healthcare
Audio-based technologies and healthcare go hand-in-hand . Research has shown that it is possible to automatically monitor lung health abnormalities through respiratory sounds. In our study, filter bank energy-based features and Random Forests can classify lung problem types from respiratory sounds. More specifically, we propose a respiratory sounds representation technique capable of modeling the dominant frequency range present in such sounds. On a publicly available dataset (ICBHI) our results are encouraging to classify respiratory sounds: crackles and wheezes.
Ravi, Krishna Kunchala, "Respiratory sound classification to analyze lung health abnormalities" (2021). IdeaFest. 335.