Goelz H, Jones R D, Bones P J
Department of Electrical and Electronic Engineering, University of Canterbury, Christchurch, New Zealand.
Clin Electroencephalogr. 2000 Oct;31(4):181-91. doi: 10.1177/155005940003100406.
Wavelet based signal analysis provides a powerful new means for the analysis of nonstationary signals such as the human EEG. The properties of the discrete wavelet transform are reviewed in illustrated application examples. The continuous wavelet transform is shown to provide better detection and representation of isolated transients. An approach to extract features of edges and transients from the continuous wavelet transform is outlined. Matching pursuit is presented as a more general transform method that covers both transients and oscillation spindles. A statistical model for the continuous wavelet transform of background EEG is found. A spike detection system based on this background model is presented. The performance of this detection system has been assessed in a preliminary clinical study of 11 EEG recordings containing epileptiform activity and shown to have a sensitivity of 84% and a selectivity of 12%. The spatial context of epileptiform activity will be incorporated to improve system performance.
基于小波的信号分析为诸如人类脑电图（EEG）等非平稳信号的分析提供了一种强大的新方法。在示例应用中回顾了离散小波变换的特性。结果表明，连续小波变换能更好地检测和表示孤立瞬变信号。概述了一种从连续小波变换中提取边缘和瞬变特征的方法。匹配追踪作为一种更通用的变换方法被提出，它涵盖了瞬变信号和振荡纺锤波。建立了背景脑电图连续小波变换的统计模型。提出了一种基于该背景模型的尖峰检测系统。在一项对11份包含癫痫样活动的脑电图记录的初步临床研究中，对该检测系统的性能进行了评估，结果显示其灵敏度为84%，选择性为12%。将结合癫痫样活动的空间背景以提高系统性能。
