Latka Miroslaw, Was Ziemowit, Kozik Andrzej, West Bruce J
Institute of Physics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland.
Phys Rev E Stat Nonlin Soft Matter Phys. 2003 May;67(5 Pt 1):052902. doi: 10.1103/PhysRevE.67.052902. Epub 2003 May 19.
Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.
人类脑电图中的发作间期棘波和尖波是癫痫的特征性信号。这些电位是由许多神经元同步病理性放电产生的。可靠检测此类电位一直是脑电图分析中的长期难题，尤其是在对癫痫患者进行长期监测变得普遍之后。传统的棘波定义基于其幅度、持续时间、尖锐程度以及与背景的差异。然而，仅基于此定义构建的棘波检测系统由于存在大量瞬变和伪迹而不可靠。我们使用小波变换来分析癫痫脑电图表现的特性。我们证明，癫痫棘波在不同尺度上的小波变换行为可以构成一种相对简单但有效的检测算法的基础。
