基于分数阶希尔伯特变换的罗音特征提取  

Crackle Feature Extraction Based on Fractional Hilbert Transform

作　　者：李真真[1] 杜明辉[2] 吴效明[1] 

机构地区：[1]华南理工大学生物科学与工程学院,广东广州510006 [2]华南理工大学电子与信息学院,广东广州510640

出　　处：《华南理工大学学报（自然科学版）》2011年第12期38-43,共6页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(81070612)

摘　　要：现有的罗音检测方法存在检测效果不理想、计算复杂度过高等不足,而分数阶希尔伯特变换对信号中的异常分量有着高度敏感性.文中在不同的分数阶将希尔伯特变换作用于罗音信号;变换后的信号表现为逐步相移.之后将原肺音信号与各阶变换结果构建相关函数,以各阶相关函数为待匹配特征,将其与标准模板相匹配,匹配系数高的判定为罗音,否则判为非罗音.仿真结果表明,罗音检测正确率达91.2%,证实了该方法是有效的.As the existing methods to detect crackles are of non-ideal detection effects and complex computation,a new method taking the advantage of high sensitivity of fractional Hilbert transform to the abnormal components of signals is proposed.In this method,for different fractional values,Hilbert transforms are employed to transform crackle signals into the signals with stepped phase shifts.Then,functions describing the correlation between the original lung sound signals and the transformed ones are obtained with respect to different fractional orders,which are considered as the features to be matched with standard templates.The detected signals with high matching co-efficients are judged as crackles,while those with low matching coefficients are judged as non-crackles.Simulated results indicate that the proposed method is effective and the detection accuracy is up to 91.2%.

关 键 词：信号处理 信号检测 计算机辅助诊断 特征提取 分数阶希尔伯特变换 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]