用于心力变化趋势评价的基于概率神经网络的心音识别算法  

Heart Sound Recognition Algorithm Based on PNN for Evaluating Cardiac Contractility Change Trend

作　　者：郭兴明[1] 颜彦[1] 姚晓帅[1] 肖守中[1] 

机构地区：[1]重庆大学生物工程学院生物力学与组织工程教育部重点实验室,重庆400044

出　　处：《生物医学工程学杂志》2006年第5期934-937,共4页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(30070211)

摘　　要：讨论了用于评估心力变化趋势的心音识别算法,包含了对不同运动条件下记录的心音样本的识别。尤其是讨论了对剧烈运动负荷后记录的心音进行的识别。提出的算法包括两个相互联系的方法。第一个是基于概率神经网络的算法,用于识别静息状态和轻微运动状态;第二个是基于心音本身特点的算法,用于对剧烈运动(本研究中约定的全运动量)后心音的识别。最后,使用该算法对45个在静息状态和轻微运动(1/4运动量)状态下记录的正常和异常心音的样本,以及28个剧烈运动后记录的心音样本进行了识别。结果表明94%的样本可被正确识别和分类。这个识别算法为后续的心音分析研究提供了可靠基础。This paper discusses the recognition of heart sound for evaluating the cardiac contractility change trend, which includes heart sound samples recorded at different exercise condition. Especially, focused on the recognition of heart sound recorded after high intensity exercise workload. The algorithm proposed consisted of two correlative methods. The first was to recognize heart sound recorded at rest and after low intensity exercise workloads by probabilistic neural network and the second was to recognize heart sound recorded after high intensity exercise workloads based on the characteristic of heart sound. Both methods have two consecutive phases. Firstly, all peaks, including the peaks of both heart sounds and noise, are marked by a repetitive threshold detecting algorithm. Secondly, probabilistic neural network is employed to classify the peaks detected in the first phase into S1, S2, and noise. Finally, the performance of the algorithm was evaluated using 45 digital heart sound recordings including normal and abnormal heart sound, which were recorded at rest and after low intensity exercise workloads, and 28 digital heart sound recordings recorded after high intensity exercise workloads. The results showed that over 94% of heart sound samples were classified and recognized correctly. Moreover, the reasons for the wrong classification, of which omitting and misdetection are two main problems, are also discussed and solutions are proposed. So this method can be improved and refined in following studies. In conclusion, this algorithm is a reliable approach to detect and classify heart sounds, providing a solid basis for further heart sound analysis.

关 键 词：心音 心音包络 概率神经网络 心力变化趋势 

分 类 号：R318[医药卫生—生物医学工程]