由于傳統的心音聽診就是憑醫生的經驗用聽覺分析心音信號, 不能滿足醫學上所要求
的高精確度性能而且聽診技能要花多年時間才能掌握,針對這些弊端本文提出了一種新的心音診斷方法。它對電子聽診器錄制的心音數據，經過去噪預處理后用小波變換進行分析并提取特征值，再將選取的特征值輸入到前饋型神經網絡進行訓練和識別。實驗中我們用節點數分別為9，5，5 的BP 神經網絡能成功識別出主動脈關閉不全，主動脈狹窄，二尖瓣關閉不全，二尖瓣狹窄，和正常心音五類心音, 能為相應心臟疾病的診斷提供有力的依據，為臨床應用提供有效的分析手段。
關鍵詞：心音 小波變換 特征值選取 神經網絡

The method of heart sound diagnosis based on neural networks and wavelet transform
Cao-Li1 ,Zhao De-an1 ,Sun Yueping1 ,Liu Jian-yue2 1. School of Electrical and Information Engineering , Jiangsu University, Zhenjiang 212013 2. Hospital of Jiangsu University, Zhenjiang 212013 ABSTRACT: Heart auscultation with the bare ear and the stethoscope can not satisfy the high precision in medicine and forming a diagnosis based on heart sounds is a skill that can take years to acquire. So this paper presents a diagnosis method on heart auscultation. A library of heart sound files, recorded via an electronic stethoscope are used, features from these samples are extracted using discrete wavelet transform and the classification is trained and carried out by using a feed forward neural network. We collected sound samples of five categories, they are aortic regurgitation, aortic stenosis, mitral regurgitation, mitral stenosis and normal heard sound.The BP can regonise them succesfully. It can make up the shortage of heart auscultation and save the time to grasp the diagnosis skill for doctors.
Key words:heard sound; wavelet transform; eigenvalue; neural networks