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Wavelet time entropy as a T-wave alternans detector in presence of noise.

机译：小波时间熵作为存在噪声的T波交替信号检测器。

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摘要

Ventricular Fibrillation (VF) is one of the most common causes of sudden cardiac death. Clinical studies were conducted to link some ECG features to the occurrence of VF. It was found that T-wave Alternans (TWA) is one of the most promising risk prediction indicators for VF. The detection of TWA will assist physicians in taking preventive measures to avoid VF.;ECG signals are not stationary signals, and are mixed with noise. Moreover, TWA magnitude is in the millivolt level. Research is ongoing to increase the sensitivity and specificity of the TWA detection algorithms. Wavelet transform based algorithms were previously developed to detect TWAs.;In this thesis, the wavelet time entropy (WTE) is proposed as a new biomedical indicator to detect alternations in T-wave shape and amplitude. Any change in the T-wave morphology is expected to be reflected in its WTE value. The outcome of this research is used to complement existing work for the purpose of sensitivity and specificity improvement VF prediction.;A total of 300 simulated and recorded life electrocardiogram (ECG) signals each were used to test the sensitivity and specificity of the algorithm before and after using WTE. The tests results showed that the increase in the algorithm sensitivity due to using WTE exceeds the decrease in specificity, which makes WTE a beneficial predictor compared to other research methods. The maximum increase in sensitivity was 21% while the corresponding decrease in specificity was only 3%, with a 4% average increase in sensitivity. The increase in sensitivity was noticed mainly for problematic cases when the TWA amplitude and the SNR are low. In the final part of the thesis we used the MRA with the sliding window technique. We found that the specificity for the MRA is 100%, while the increase in the sensitivity compared with RA is up to 20%.
机译：心室纤颤（VF）是心源性猝死的最常见原因之一。进行了临床研究，以将某些ECG功能与VF的发生联系起来。研究发现，T波交替链（TWA）是VF最有前途的风险预测指标之一。 TWA的检测将帮助医生采取预防措施来避免VF。； ECG信号不是平稳信号，并且与噪声混合。此外，TWA幅度处于毫伏级别。为了提高TWA检测算法的灵敏度和特异性，正在进行研究。先前已经开发了基于小波变换的算法来检测TWA。本文将小波时间熵（WTE）作为一种新的生物医学指标来检测T波形状和振幅的变化。 T波形态的任何变化都有望反映在其WTE值中。这项研究的结果用于补充现有工作，以提高敏感性和特异性VF预测的目的。；总共使用300个模拟和记录的生命心电图（ECG）信号分别测试算法的敏感性和特异性。使用WTE之后。测试结果表明，由于使用WTE，算法灵敏度的提高超过了特异性的降低，与其他研究方法相比，WTE是一种有益的预测指标。敏感性的最大增加为21％，而相应的特异性降低仅为3％，平均敏感性增加4％。灵敏度提高主要是在TWA振幅和SNR低的问题情况下注意到的。在论文的最后部分，我们将MRA与滑动窗口技术结合使用。我们发现，MRA的特异性为100％，而与RA相比，敏感性提高了20％。

著录项

  • 作者

    Al-Azzeh, Mohammad Lutfi.;

  • 作者单位

    Tennessee Technological University.;

  • 授予单位 Tennessee Technological University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 M.S.
  • 年度 2014
  • 页码 92 p.
  • 总页数 92
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地下建筑;
  • 关键词

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