Modified clustering algorithm for the trachea sound featuring

Menglun Hsueh,Yucheng Wu,Fokching Chong,Bingyuh Lu,Shuiken Wei,Hueydong Wu

System Science and Engineering（2010）

Cited 1|Views10

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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.

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Key words

acoustic signal detection,pattern clustering,k-means clustering algorithm,modified clustering algorithm,signal-to-noise-ratio,spectrograms,trachea sound featuring,wheeze detection,snr,filter,k-means clustering,segment,spectrogram,wheeze sounds,biology,k means clustering algorithm,k means clustering,indexing,signal to noise ratio,nickel

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