Localizing Heart Sounds in Respiratory Signals Using Singular Spectrum Analysis
Localizing Heart Sounds in Respiratory Signals Using Singular Spectrum Analysis
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DOI：
10.1109/tbme.2011.2162728
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发表时间：
2011-12-01
影响因子：
4.6
通讯作者：
Sanei, Saeid
中科院分区：
文献类型：
--
作者：
Ghaderi, Foad;Mohseni, Hamid R.;Sanei, Saeid
呼吸音总是被心音干扰所污染。在一些心音消除方法中的基本预处理步骤是定位主要心音分量。奇异谱分析（SSA），一个强大的时间序列分析技术，在本文中使用。尽管心脏和肺的声音分量的频率重叠，两个不同的趋势，在本征值谱是可识别的，这导致找到一个子空间，包含更多的信息的基础心音。混合呼吸信号和真实的呼吸信号用于评估该方法的性能。选择合适的SSA窗口长度可以获得较好的分解质量和较低的计算代价。所提出的方法的结果进行了比较与那些行之有效的方法，它使用小波变换和熵的信号来检测心音分量。所提出的方法优于基于小波的方法在错误检测和相关性与潜在的心音。所提出的方法的性能略优于基于熵的方法。而且，前者的执行时间明显低于后者。
Respiratory sounds are always contaminated by heart sound interference. An essential preprocessing step in some of the heart sound cancellation methods is localizing primary heart sound components. Singular spectrum analysis (SSA), a powerful time series analysis technique, is used in this paper. Despite the frequency overlap of the heart and lung sound components, two different trends in the eigenvalue spectra are recognizable, which leads to find a subspace that contains more information about the underlying heart sound. Artificially mixed and real respiratory signals are used for evaluating the performance of the method. Selecting the appropriate length for the SSA window results in good decomposition quality and low computational cost for the algorithm. The results of the proposed method are compared with those of well-established methods, which use the wavelet transform and entropy of the signal to detect the heart sound components. The proposed method outperforms the wavelet-based method in terms of false detection and also correlation with the underlying heart sounds. Performance of the proposed method is slightly better than that of the entropy-based method. Moreover, the execution time of the former is significantly lower than that of the latter.