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首页> 外文学位 >Heart sound reduction from lung sound recordings applying signal and image processing techniques in time-frequency domain.
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Heart sound reduction from lung sound recordings applying signal and image processing techniques in time-frequency domain.

机译：通过在时频域中应用信号和图像处理技术，从肺部录音中减少心音。

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摘要

In this study, two novel HS cancellation methods, using spectrogram Independent Component Analysis (ICA)-based technique and spectrogram filtering-based method in Time-Frequency (TF) domain along with three new techniques for HS localization in respiratory sound recordings are presented. To separate HS from lung sound, the spectrogram ICA-based method applies the ICA algorithm independently to every frequency on the spectrogram of two simultaneously lung sound recordings from two different locations on the chest and yields the independent components at that frequency. Then the proper independent components from each frequency are chosen and combined with each other to produce the spectrogram of separated signals. By implementing Inverse Short Time Fourier Transform (ISTFT), the separated signals are reconstructed in time domain. On the other hand, the spectrogram filtering-based method detects the HS-included segments in the spectrogram of a recorded lung sound signal using one of the proposed HS localization techniques. Afterwards, the algorithm removes those segments and estimates the missing data via a 2D interpolation in the TF domain. Finally, the signal is reconstructed into the time domain. The efficiency of the proposed methods for HS localization and HS cancellation from lung sound recordings was examined quantitatively and qualitatively by visual and auditory means. The results show that the spectrogram ICA-based method is promising in term of HS reduction from lung sound recordings and the spectrogram filtering-based method successfully removes HS from lung sound signals, while preserving the original fundamental components of the lung sounds. The computational cost and the speed of both proposed methods were found to be much more efficient than other HS reduction methods. (Abstract shortened by UMI.).
机译：在这项研究中，提出了两种新颖的HS消除方法，分别使用基于频谱图独立分量分析（ICA）的技术和基于频谱图滤波的时频（TF）域方法，以及三种在呼吸声记录中进行HS定位的新技术。为了从肺声中分离HS，基于频谱图ICA的方法将ICA算法独立地应用于来自胸部两个不同位置的两个同时出现的肺声记录的频谱图上的每个频率，并在该频率上产生独立的分量。然后从每个频率中选择适当的独立分量，并将它们彼此组合以产生分离信号的频谱图。通过实施逆短时傅立叶逆变换（ISTFT），可以在时域中重构分离的信号。另一方面，基于频谱图滤波的方法使用一种拟议的HS定位技术来检测记录的肺部声音信号的频谱图中包含HS的片段。之后，该算法将删除这些段，并通过TF域中的2D插值来估计丢失的数据。最后，信号被重构到时域中。通过视觉和听觉手段定量和定性地检查了所提出的用于HS定位和从肺部录音中消除HS的方法的效率。结果表明，基于频谱图ICA的方法在降低肺音记录中的HS方面很有希望，基于频谱图过滤的方法成功地从肺声信号中删除了HS，同时保留了肺音的原始基本成分。两种提议方法的计算成本和速度都比其他HS降低方法高效得多。 （摘要由UMI缩短。）。

著录项

  • 作者

    Talebpourazad, Mahsa.;

  • 作者单位

    University of Manitoba (Canada).;

  • 授予单位 University of Manitoba (Canada).;
  • 学科 Engineering Biomedical.;Engineering Electronics and Electrical.
  • 学位 M.Sc.
  • 年度 2004
  • 页码 81 p.
  • 总页数 81
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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