摘要
　　胸腔聽診為診斷肺部病症的主要方法，醫生藉由聽診器聽取肺部聲音，憑藉其專業認知與經驗來判斷不同的肺音所代表的病症。在120 Hz以下的生理訊號是由心音與肺音組成，而人耳對於低頻的靈敏度不高，故造成醫生在聽診判斷上的困難。為解決此一問題，本研究的目的為建構多種肺音辨識系統，用來辨識肺泡音(vesicular breath sounds)，支氣管音(bronchial breath sounds)， 氣管音(tracheal breath sounds)，爆裂音(crackle)，哮喘音(wheeze)，喘鳴音(stridor)等六種常見肺音。
首先使用壓電麥克風與資料擷取卡NI-PXI 4472B擷取人體肺音訊號，並作訊號預處理。接著以小波轉換作為特徵擷取之方法，透過圖形監控軟體LabVIEW 設計小波轉換之架構，訊號分解後之六個頻段做標準差與平均值運算，以得十七個特徵值。在分類器方面，本研究以倒傳遞與學習向量量化類神經網路作為系統分類器之子系統，用以模擬網路之可行性與內部參數，再經由LabVIEW建構類神經網路，分別測試其網路分類率，最後整合各子系統並建構二階段式類神經網路，以提升系統之可靠度。由實驗結果顯示，相較於傳統聽診方式，本研究成功建構出一套多種肺音診斷系統，可正確地分類出六種常見肺部聲音，彌補人耳對於低頻靈敏度不高的缺點，並由圖形監控軟體LabVIEW建構人機介面，顯示肺音之頻譜、登記病歷資料等，可供醫生作為診斷肺部疾病病患之輔具。其結果顯示，本研究所建構之系統其辨識率可達95%。
關鍵詞：肺音聽診、類神經網路、小波轉換、接受器操作特性曲線。

Chest auscultation is a main and efficient way to diagnose lung disease, it is a subjective process that depending on the physician’s experience and ability to differentiate between different sound patterns. Because physiological signals composed of heart sound and pulmonary sound are above 120HZ and the in sensitive of the human ear to the lower frequency, it is not easy to make diagnostic classification successful. In order to solve this problem, this study aims to construct a variety of pulmonary sound (PS) recognition system for classification of six different PS classes: Vesicular breath sounds, bronchial breath sounds, tracheal breath sounds, crackles, wheezes, stridor sounds.
First, we use the piezoelectric microphone and data acquisition card NI-PXI 4472B to acquire PS signals, and signals preprocessing. The wavelet transform as feature extraction method, the PS signals were decomposed into the frequency subbands. Through statistical method we get the seventeen feature vectors which are used the neural network's input vector. This research used back-propagation (BP) neural network and learning vector quantization (LVQ) neural network to be subsystem, and the two neural networks are integrated together as a two stage system that can increase the reliability. The neural networks' performance is verified by the receiver operating characteristic (ROC) curve. Comparing with traditional auscultation method, this study successfully construct a variety of pulmonary sound diagnostic system can correctly classify the six common pulmonary sounds. In this study, can be improved that human ear’s insensitive to the lower frequency, and show its pulmonary sounds wave, characteristic value and spectral analysis chart are shown by the human-machine interface design. By the research of this paper, the recognition rate of system is up to 95%.

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