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Orthonormal-Basis Partitioning And Time-Frequency Representation of Non-Stationary Signals

机译:非平稳信号的正交基划分和时频表示

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摘要

Spectral analysis is important in many fields, such as speech, radar and biomedicine. Many signals encountered in these areas possess time-varying spectral characteristics. The power spectrum indicates what frequencies exist in the signal but it does not show when those frequencies occur. Time-frequency analysisprovides this missing information. A time-frequency representation of the signal shows the intensities of the frequencies in the signal at the times they occur, and thus reveals if and how the frequencies of a signal are changing over time.Time-dependent spectral analysis of beat-to-beat variations of cardiac rhythm, or heart rate variability (HRV), represents a major challenge due to the structure of the signal. A number oftime-frequency representations have been proposed for the estimation of the time-dependent spectra. However, time-frequency analysis of multicomponent physiological signals such as cardiac rhythm is complicated by the presence of numerous, ill-structured frequency elements. We sought to develop a simple method for 1)detecting changes in the structure of the HRV signal, 2)segmenting the signal into pseudo-stationary portions, and 3)exposing characteristic patterns of the changes in thetime-frequency plane. The method, referred to as Orthonormal-Basis Partitioning and Time-Frequency Representation (OPTR), is validated on simulated signals and HRV data. Unlike the traditional time-frequency HRV representations, which are usuallyapplied to short segments of signals recorded in controlled conditions, OPTR can be applied to long and "content-rich" ambulatory signals to obtain the signal representation along withits time-varying spectrum. Thus, the proposed approach extends the scope of applications of the time-frequency analysis to all types of HRV signals and to other physiological data.
机译:频谱分析在许多领域都很重要,例如语音,雷达和生物医学。在这些区域遇到的许多信号都具有随时间变化的频谱特性。功率谱指示信号中存在哪些频率,但不显示这些频率何时出现。时频分析提供了这种丢失的信息。信号的时频表示显示了信号在发生时的频率强度,从而揭示了信号的频率是否以及如何随时间变化。由于信号的结构,心律的变化或心率变异性(HRV)代表了一项重大挑战。已经提出了许多时频表示来估计与时间有关的频谱。但是,由于存在许多结构不良的频率元素,因此对多成分生理信号(如心律)进行时频分析变得很复杂。我们试图开发一种简单的方法,用于:1)检测HRV信号的结构变化; 2)将信号分割为伪平稳部分; 3)暴露时频平面变化的特征模式。该方法被称为正交基分割和时频表示(OPTR),在模拟信号和HRV数据上得到了验证。与通常应用于在受控条件下记录的信号的短段的传统时频HRV表示法不同,OPTR可以应用于长且“内容丰富”的动态信号,以获得随时间变化的频谱的信号表示。因此,所提出的方法将时频分析的应用范围扩展到了所有类型的HRV信号和其他生理数据。

著录项

  • 作者

    Aysin Benhur;

  • 作者单位
  • 年度 2002
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

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