Wheeze Detection using Modified k-Means Clustering Algorithm
Date Issued
2011
Date
2011
Author(s)
Hsueh, Meng-Lun
Abstract
The aim of this study is to present wheeze time-frequency characteristics in color scales spectrogram, and k-means clustering algorithm is applied to detect wheezes. k-means clustering algorithms are grouped according to its spectrogram nature. The first step is to preset the k value, representing the grouping number. After experiment testing, the k value is set to three. This number corresponds to the color scale spectrogram are red, green, and blue. Wheeze sounds can also be displayed on the spectrogram. However, k-means clustering algorithms group number is assigned randomly. Therefore, the color corresponding to wheezing symptoms has no fixed color. Through the color-indexing method, the wheezing color is set to be red in accordance with the color index production proportions.
In addition, this method is also applied to the normal respiratory sounds, and the effects of noise reduction are discussed. After using modified k-means clustering algorithm, the results show that the signal-to-noise ratios are improved for about 2dB for wheeze and normal cases. The color index can mark wheezing sounds on the color spectrogram in red, and this has a stable representation and reproducibility. This helps the doctor very much in wheeze detection.
Subjects
spectrogram
wheezes
k-means clustering algorithm
color-indexing
signal-to-noise ratio
reproducibility
Type
thesis
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