Modified clustering algorithm for the trachea sound featuring
Journal
2010 International Conference on System Science and Engineering, ICSSE 2010
Pages
212-215
Date Issued
2010
Author(s)
Abstract
This study attempts to detect wheeze with k-means clustering algorithm. The subjects which included normal and wheeze sounds were evaluated. The algorithm presented a good performance to filter out noise and segment wheeze episode regions in the spectrograms. The results show that the clustering algorithm improved the signal-to-noise-ratio (SNR) from 29.02 ± 8.65 to 30.49 ± 9.01 dB in the cases of normal subjects, and 36.99 ± 9.67 to 38.29 ± 10.10 dB in the ones of wheeze subjects.
Type
conference paper