心音降噪声与心音分割方法研究

Methods on Noise Reduction and Segmentation of Heart Sound Signal

作者: 专业:生物医学工程 导师:唐洪 年度:2010 学位:硕士  院校: 大连理工大学

Keywords

Heart Sound Signal, Clustering, Noise Reduction, Time-frequency Analysis, Segmentation of Heart Sound

        心脏听诊是诊断心血管疾病的一种重要方法。然而,听诊在很大程度上受到医生主观性和听诊技能的影响,且听诊检查的效率低下。因此,有必要利用计算机辅助诊断。心音图(PCG)可以提供很多有关心脏瓣膜功能和心脏血流动力学的有价值的信息,对辅助检测诊断各种心脏疾病很有帮助。心音信号为非平稳时变信号,在采集的过程中很容易被噪声污染。本文结合心音的医学特征信息,在时频域内提出一种心音的去噪声方法。首先利用改进的匹配追踪算法将含有噪声的心音信号分解为一系列表征为幅度、时间、频率等参数的原子。根据心音信号的医学特征,在时频域内用聚类算法分离出表示心音的原子和表示噪声的原子。由表示心音的原子重构出心音信号,实现心音信号去噪声。本文对多个心血管疾病的病例进行了仿真实验,经去噪后的重构信号与原信号的相似度均超过了0.9,验证了去噪方法的有效性。心音是心脏瓣膜活动及相关组织振动的综合结果。心音的持续时间、幅度、频率等参数反映了心血管工作情况。分割出心音是进行计算机自动分析的重要步骤。本文在时频域内根据心音的特征提出了一种方法分割出第一心音和第二心音。将心音信号分解到时频域,根据心音在时域、频域的医学特征,提出一套分割出第一心音和第二心音的策略。首先通过时频分析得到心音信号的时频分析图,确定心音在时间、频率平面内各成分对应的区域信息。通过搜索并判断不同区域所在的范围,进而确定各个范围对应的心音信号成分。通过对各个心音信号成分的取舍得到第一心音和第二心音的分割结果。本文对多种病例的心音信号进行了分割实验,实验结果表明该方法识别分段正确率达到了90%以上,为下一步心音的分析和疾病的诊断提供了重要依据。
    Heart auscultation is an important method for diagnosing heart valve diseases. The auscultation, however, is always effected by doctors’ subjectivity and cardiac auscultatory skills. So it’s necessary to take advantage of the computer to assist the diagnosis. Phonocardiogram (PCG) can provide valuable information about heart valve function and cardiac hemodynamics, and assist doctors in detecting and diagnosing various heart diseases.Heart sounds are no stationary, transient signals. They are easily contaminated by noise. This paper proposes a new method in joint time, frequency, medicine domains to reduce noise. Heart sound recording is decomposed into a series of time-frequency atoms, characterized by amplitude, time, frequency, duration by the modified matching pursuit method. From the features of heart sound in medical domain, it is found that heart sounds behave as clusters in time-frequency domains in nature. A clustering method is thus employed to distinguish the atoms for heart sounds. Meanwhile, the atoms for noise, which are out of clusters, are rejected. Experiments on various heart disease’s cases are made, and the similarity between the original signal and the reconstructed signal exceed 90%, which shows the validity of this de-noise method.Heart sound is generated by the combined results of cardiac valves activities and related vibrations. The parameters of duration, amplitude, frequency reflect the working conditions of cardiovascular. Segmentation of heart sound is an important step for automatic analysis by computer. In this paper, we propose a method to segment the first heart sound and the second heart sound in time-frequency domain according to heart sound’s characteristics. Firstly, a time-frequency plane is gained by time-frequency analysis, and then time displacement and frequency information of each component are determined. We can make certain different regions by searching and determining the previous information and thus to establish the scope of the corresponding components of heart sound. We have made experiments on the heart sound signal from many heart disease’s cases. The results of segments for S1 and S2 are satisfactory. The method proposed in this paper can provide an important basis for the next step of analysis and diagnosis of heart sounds.
        

心音降噪声与心音分割方法研究

摘要4-5
Abstract5
1 绪论8-14
    1.1 研究背景与意义8-12
        1.1.1 心音信号处理方法的理论8-10
        1.1.2 国内外的研究现状10-12
    1.2 面临的问题及解决的方法12
    1.3 本文的研究内容12-13
    1.4 本文的结构安排13-14
2 心音信号的相关基础知识14-22
    2.1 心音信号的产生机理14-17
        2.1.1 心脏的基本结构14-15
        2.1.2 心音的产生15-17
    2.2 心音信号的组成17-19
    2.3 心音信号的特征19-22
        2.3.1 心音信号的时域特性19-20
        2.3.2 心音信号的频域特性20-22
3 心音信号的降噪声方法22-33
    3.1 心音信号的噪声特征22
    3.2 信号的分解与重构22-25
        3.2.1 匹配追踪算法22-24
        3.2.2 改进的匹配追踪算法24-25
    3.3 基于聚类的去噪处理25-28
    3.4 实验结果与讨论28-31
        3.4.1 正常心音信号去噪声处理28-30
        3.4.2 第二心音分裂的去噪声处理30-31
        3.4.3 对多种病例的心音去噪效果统计31
    3.5 本章小结31-33
4 时频域内的心音分割方法33-48
    4.1 时频域内信号的分解与重构33-38
    4.2 判断并识别心音成分38-43
        4.2.1 确定心音成分在时频图上的范围38-40
        4.2.2 判断并分割S1、S240-41
        4.2.3 算法的流程41-43
    4.3 仿真实验与结果讨论43-47
        4.3.1 对正常心音信号的分割处理43-44
        4.3.2 对二尖瓣狭窄信号的分割处理44-45
        4.3.3 对第二心音分裂信号的分割处理45-46
        4.3.4 实验结果与讨论46-47
    4.4 本章小结47-48
结论48-50
参考文献50-53
攻读硕士学位期间发表学术论文情况53-54
致谢54-56
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