机械瓣心音频谱分析与基于改进LDB算法的识别  

Spectral Analysis and LDB Based Classification of Heart Sounds with Mechanical Prosthetic Heart Valves

作　　者：张地[1,2] 吴跃泉[2] 姚尖平[3] 杨嵩[3] 杜明辉[2] 

机构地区：[1]韶关学院计算机科学学院,韶关512005 [2]华南理工大学电子与信息学院,广州510641 [3]中山大学附属第一医院心脏外科,广州510080

出　　处：《生物医学工程学杂志》2011年第6期1207-1212,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(60772117);广东省自然科学基金资助项目(07006491)

摘　　要：听诊是通过听取心脏所发出的声音来帮助诊断各种心脏疾病的一种有效手段。鉴于目前机械瓣的使用非常普遍,研究简单有效的机械瓣病变判别方法对于临床诊断来讲具有很大的意义。针对五种不同的机械瓣心音进行的分析表明,运用频谱仅能鉴别瓣周漏这一种机械瓣病变。虽然直接利用信号的时频成分进行机械瓣心音分类是可能的,但识别率只有84.0%。利用改进的局部最优基(LDB)算法来提取特征对机械瓣心音分类有着非常大的帮助,识别率达到了97.3%。与原始的LDB算法相比,实验表明改进后的LDB算法对提高识别率和降低计算复杂性都有着明显的优势。Auscultation, the act of listening for heart sounds to aid in the diagnosis of various heart diseases, is a widely used efficient technique by cardiologists. Since the mechanical prosthetic heart valves are widely used today, it is important to develop a simple and efficient method to detect abnormal mechanical valves. The study on five different mechanical valves showed that only the case of perivalvular leakage could be detected by spectral estimation. Though it is possible to classify different mechanical valves by using time-frequency components of the signal directly, the recognition rate is merely 84%. However, with the improved local discriminant bases （LDB） algorithm to extract features from heart sounds, the recognition rate is 97. 3%. Experimental results demonstrated that the im- proved LDB algorithm could improve classification rate and reduce computational complexity in comparison with original LDB algorithm.

关 键 词：心音 人工机械瓣 功率谱估计 局部最优基 

分 类 号：R541[医药卫生—心血管疾病]