首 页    学报简介    作者投稿    专家审稿    编辑办公    读者须知    联系我们
引用本文格式: 曾劲云,何培宇,潘帆. 基于自适应阈值的心音分段算法研究 [J]. 四川大学学报: 自然科学版, 2019, 56: 867~874.
 
基于自适应阈值的心音分段算法研究
Research on heart sound segmentation algorithm based on adaptive threshold
摘要点击 64  全文点击 22  投稿时间:2018-09-21  修订日期:2019-03-18
查看全文  查看/发表评论  下载PDF阅读器
DOI编号   
中文关键词   心音分段  小波变换  香农能量包络  峰值检测  迭代法
英文关键词   Heart sound segmentation  Wavelet transform  Shannon energy envelope  Peak detection  Iterative method
基金项目   四川省科技支撑资助项目(2011SZ0123,2013GZ1043)
作者单位E-mail
曾劲云 四川大学电子信息学院 849254586@qq.com 
何培宇 四川大学电子信息学院 hepeiyu@scu.edu.cn 
潘帆 四川大学电子信息学院  
中文摘要
    心音信号可以反映人体心脏瓣膜活动情况,对心音进行分类可以区别出不同心音的病理性信息,这对于临床上诊断不同的心脏疾病具有重要的意义.心音分段是进行心音分类的前提,通过心音分段可以定位出心音中的第一心音(S1)和第二心音(S2),为心音特征参数提取与心音分类提供定位基准.为此,本文提出了一种新的自适应阈值选取心音分段算法.该方法首先利用小波变换默认阈值法对心音信号进行去噪;然后使用归一化香农能量来提取较为平滑的心音包络;接着对包络进行有效地峰值检测,从而确定初始大阈值TH1,并通过迭代法得到最终稳定的双阈值;最后进行心音分段以及分段结果分析.针对部分异常心音分段结果,如心音分裂等的分段结果,利用心音时域、能量等特性实现心音段的合并或去除,保证了分段结果的准确性.实验结果表明,本文方法对正常及异常心音分段准确率分为97.24%和91.83%,总体分段准确率为95.56%,分段准确率高于传统的阈值选取分段方法.
英文摘要
    Heart sound signal can reflect the activity of the human heart valve, and the heart sound classification can distinguish the pathological information of different heart sounds, which is of great significance for clinical diagnosis of different heart diseases. Heart sound segmentation is the premise of heart sound classification. The heart sound segmentation can locate the first heart sound (S1) and the second heart sound (S2) in the heart sound, and provide a positioning reference for heart sound feature parameter extraction and heart sound classification. For this reason, a new adaptive threshold selection heart sound segmentation algorithm is proposed in this paper. This method first uses the wavelet transform default threshold method to denoise the heart sound signal; then uses the normalized Shannon energy to extract a smoother heart sound envelope; then performs effective peak detection on the envelope to determine the initial large threshold TH1, and the final stable double threshold is obtained by an iterative method; finally, heart sound segmentation and the segmentation result analysis are performed. For partial abnormal heart sounds segmentation results, such as the segmentation result of heart sound splitting, the heart sound segment is merged or removed by using the characteristics of heart sound time domain and energy, which ensures the accuracy of the segmentation result. The experimental results show that the segmentation accuracy of normal and abnormal heart sounds is 97.24% and 91.83%, and the overall segmentation accuracy is 95.56%, which is higher than the traditional threshold selection segmentation method.

您是第 3401327 位访问者

版权所有 @ 2007《四川大学学报 (自然科学版)》编辑部
地址: 四川省成都市武侯区四川大学望江校区文科楼330至342室  邮编: 610064
电话: (028)85410393  传真: (028)85410393  E-mail: scdx@scu.edu.cn
本系统由北京勤云科技发展有限公司设计