動靜脈瘻管的檢測方式通常是透過感覺觸覺的刺激及脈動，或是利用聽診器、督卜勒超音波、血管造影等。然而這些技術皆需要特定的設備和操作者。聲學是一種辨識血管直徑變化的非侵入性方法。在本研究中，建立了一套仿生系統來簡化血流狀況的模擬。聲音的訊號經由電子聽診器紀錄而後進一步做信號處理。聲學和病變的相關已在許多文獻被證實，血管通路的早期診斷，如狹窄或血栓形成是非常重要的問題。本研究的目的是利用聲學的技術來評估動靜脈瘻管狹窄程度，作為分類瘻管阻塞的指標。採用經驗模態分解方法分析阻塞程度和頻譜之間的關係，擷取出特徵後用機器學習去做訓練分類和預判，並利用臨床上的標準都卜勒超音波來做驗證。在22個案例中KNN和SVM分別展現出90.9%和85.7%的準確率，證實經驗模態分解於特徵擷取是可行的。這項非侵入性的評估方法對於家庭照顧及早期檢測是相當具有潛力。

The AV access is usually evaluated by feeling thrill and pulsation through palpation, listening for the bruit by using a stethoscope, Doppler ultrasound imaging, or angiography, etc. However, these techniques require specific equipment and operator. Phonoangiography is a noninvasive tool for identifying vascular diameter change. In this study, a mock model has been set up to simplify the simulation of blood flow condition. Phonographic signal is recorded by electronic stethoscope and further signal processed. The relationship of phonographic signals and stenotic lesions is studied. Early detection of hemodialysis access problems such as stenosis and thrombosis is very important issue. The purpose of this study is to develop a phonographic system to evaluate arteriovenous shunt (AVS) stenosis of hemodialysis patients. The degree of stenosis (DOS) is used as an index to classify the AV access condition, and is determined by the narrowing percentage of normal vessels. The empirical mode decomposition (EMD) method is applied to analyze the relationship between DOS and spectrogram. After feature extraction, use machine learning to train prediction model and classify. Verification is based on Doppler ultrasound which is the golden standard in clinical application. In 22 cases, KNN and SVM show 90.9% and 85.7% accuracy respectively, it proved that empirical mode decomposition is feasible in feature extraction. This noninvasive method may be useful and potential for early detection in home-care use.

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