本論文乃應用聲音訊號與影像特徵分類技術分析於不同病徵之肺部疾病診斷系統。
傳統上，病患疾病看診的過程中，醫護人員憑藉醫療儀器所檢查的結果，診斷病患疾病，大多數仍仰賴醫護人員的經驗進行診斷。隨著科技生活的進步，本研究旨在以智慧型分類技術來建構一套運用在肺音疾病診斷分類之智慧型聽診器系統，以期多方確認病患肺部病徵，協助醫護人員更加正確判斷病患肺部疾病，降低誤診的機率。
在進行診斷分類之前，整合諧波/撞擊聲分離(Harmonic/Percussive Sound Separation)技術，分離各個病徵頻譜圖上的諧波(水平)方向和撞擊(垂直)方向，進一步擷取出一些時間域和頻率域中的聲音訊號與影像特徵。應用特徵選取(Feature Selection)篩選出較具影響力之特徵，並以此執行分類。透過最近鄰居分類法(KNNC)、支持向量機(SVM)和隱藏馬可夫模型(HMM)，來對不同病徵之肺部疾病進行分類，在訓練分類器的階段中，可透過機器學習對訓練音訊建立其特徵空間模型，而在接下來的測試階段，可計算測試音訊的特徵來與訓練音訊之特徵空間模型進行比對。在分類的實驗結果中顯示了本聽診器系統對於各種肺病音訊的自動分類最好的準確率超過九成。

An intelligent stethoscope system that makes use of feature extraction and classification of sound signals for lung conditions is presented in this thesis. First, the method of harmonic/percussive sound separation (HPSS) separate a monaural lung sound signal into harmonic and percussive components and then calculate the features on separated signals which is helpful to classification. Each separated component, the acoustic features are extracted by using Mel-scale frequency cepstral coefficient (MFCC). Then, audio signals are converted into spectrograms to extract texture features from time and frequency image features which are then used for lung conditions prior to classification. The texture features are based on gray level co-occurrence matrix (GLCM) and local binary patterns (LBP) which have been proved to be a promising method for image classification research. In order to find the most discriminative features the method are used with sequential forward selection (SFS) and k-nearest-neighbor classifier (KNNC) as the classifier. Finally, based on these features, three classification approaches are employed, including k-nearest-neighbor classifier (KNNC), support vector machine (SVM), and hidden markov models (HMM). The experimental results reveal that the performance of features with above technique is satisfactory, for lung conditions diagnosis.

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