【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。更多还原

【Abstract】 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 improved LDB algorithm could improve classification rate and reduce computational complexity in comparison with original LDB algorithm.更多还原