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首页> 外文学位 >Separation of spiky transients in EEG/MEG using morphological filters in multi-resolution analysis.
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Separation of spiky transients in EEG/MEG using morphological filters in multi-resolution analysis.

机译：在多分辨率分析中使用形态学过滤器分离脑电图/脑电图中尖峰瞬变。

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摘要

Epileptic electroencephalographic (EEG) data often contains a large number of sharp spiky transient patterns which are diagnostically important. Background activity is the EEG activity representing the normal pattern from the brain. Transient activity manifests itself as any non-structured sharp wave with dynamically short appearance as distinguished from the background EEG. Generally speaking, the amplitude change of background activity varies slowly with time and spiky transient activity varies quickly with pointed peaks.; In this thesis, a method has been developed to automatically extract transient patterns based on morphological filtering in multiresolution representation. Using a simple structuring element (SE) to match a signal's geometrical shape, mathematical morphology is applied to detect the differences of morphological characteristics of signals. If a signal contains features consistent with the geometrical feature of the structuring element, a morphological filter can recognize and extract the signal of interest. The multiresolution scheme can be based on the wavelet packet transform which decomposes a signal into scaling and wavelet coefficients of different resolutions. The morphological separation filter is applied to these coefficients to produce two subsets of coefficients for each coefficient sequence: one representing the background activity and the other representing the transients. These subsets of coefficients are processed by the inverse wavelet transform to obtain the transient component and the background component. Alternatively, a morphological lifting scheme has been proposed for separation these two components. Experimental results on both synthetic data and real EEG data have shown that the developed methods are highly effective in automatic extraction of spiky transients in the epileptic EEG data.; The interictal spike trains thus extracted from multiple electrode recordings are further analyzed. Their cross-correlograms are examined according to the stochastic point process model. Our experiment result has been verified by human experts' estimation.
机译：癫痫性脑电图（EEG）数据通常包含大量尖锐的刺突瞬态模式，这对诊断很重要。背景活动是代表脑部正常模式的EEG活动。短暂活动表现为任何具有动态短外观的非结构性尖波，与背景脑电图有所区别。一般而言，背景活动的幅度变化随时间缓慢变化，尖峰的瞬态活动随尖峰快速变化。本文提出了一种基于形态学滤波的多分辨率表示自动提取瞬态模式的方法。通过使用简单的结构元素（SE）来匹配信号的几何形状，可以应用数学形态学来检测信号形态特征的差异。如果信号包含与结构元素的几何特征一致的特征，则形态过滤器可以识别并提取感兴趣的信号。多分辨率方案可以基于小波包变换，该小波包变换将信号分解为不同分辨率的缩放系数和小波系数。将形态分离滤波器应用于这些系数，以为每个系数序列生成两个系数子集：一个代表背景活动，另一个代表瞬态。通过逆小波变换处理这些系数子集，以获得瞬态分量和背景分量。或者，已经提出了一种形态学提升方案来分离这两个组分。对合成数据和实际EEG数据的实验结果表明，所开发的方法在癫痫EEG数据的尖峰瞬变的自动提取中非常有效。从多个电极记录中提取出的尖峰脉冲串将被进一步分析。根据随机点过程模型检查了它们的互相关图。我们的实验结果已通过人类专家的估计得到验证。

著录项

  • 作者

    Pon, Lin-Sen.;

  • 作者单位

    University of Pittsburgh.;

  • 授予单位 University of Pittsburgh.;
  • 学科 Biology Neuroscience.; Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2002
  • 页码 158 p.
  • 总页数 158
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 神经科学;无线电电子学、电信技术;
  • 关键词

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