Document Type
Poster
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Publication Date
4-2021
Keywords
respiratory, lung, health abnormalities, healthcare
Abstract
Audio-based technologies and healthcare go hand-in-hand [1]. 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.
First Advisor
KC Santosh
Research Area
Computer Science
Recommended Citation
Ravi, Krishna Kunchala, "Respiratory sound classification to analyze lung health abnormalities" (2021). IdeaFest. 335.
https://red.library.usd.edu/idea/335