利用修正k群聚演算法偵測哮鳴

Abstract

本論文主要是使用彩色聲譜圖來呈現哮鳴的時頻特徵，並利用k群聚演算法來偵測哮鳴。k群聚演算法主要執行的是分群的工作，其中，群聚組數必須人為先做設定，經過測試後，k值選擇設定成三，三可以在彩色聲譜圖中，對應到紅、綠、藍三個顏色，也可以在聲譜圖上顯示哮鳴。但是k群聚演算法中群組編號的分派是隨機的，因此對應到哮鳴病徵的顏色會產生不固定的現象，本研究利用色彩索引法來修正k群聚演算法，將三個群組編號依照在聲譜圖中所佔的面積比例製作成索引，再將哮鳴索引出來，並標示成紅色。 除此之外，此方法也應用至正常的呼吸音，雜訊消除的效應也一起被探討。結果顯示，經過修正k群聚演算法，不論是正常或哮鳴，訊號-雜訊比都約可提升2dB，同時，色彩索引的製作可以使得哮鳴在彩色聲譜圖上能夠具有穩定的再現性，才能夠提供給醫生，並且能對醫生有幫助。

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.