Trained wavelets used to detect epileptic spikes

Stefan Popescu Ph.
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引用次数: 7

Abstract

Although many authors developed different methods for automatic detection of epileptic spikes, so far a method for clinical routine has not yet evolved. Moreover the ambiguous definition of these waves makes the detection more difficult. This paper presents a new procedure to automatically detect and localise the interictal epileptic spikes based on their shape and duration. We used the discrete wavelet transform based on many trained children of a mother wavelet function to build a two-dimensional discrete wavelet spectrum. We suggest thereafter to use this spectrum in order to localise the spikes and to measure their duration. By filtering the spectrum one can easily achieve the automatic spike selection based on a duration criterion. To train the wavelets we used the conjugate gradient method commonly used until now in the field of artificial neural networks.
经过训练的小波,用于检测癫痫的尖峰
虽然许多作者开发了不同的方法来自动检测癫痫尖峰,但迄今为止尚未发展出一种用于临床常规的方法。此外,这些波的模糊定义使探测更加困难。本文提出了一种基于癫痫发作间期峰的形状和持续时间自动检测和定位的新方法。我们使用基于母小波函数的多个训练子波的离散小波变换来构建二维离散小波谱。此后,我们建议使用该频谱来定位峰值并测量其持续时间。通过对频谱进行滤波,可以很容易地实现基于持续时间标准的自动尖峰选择。为了训练小波,我们采用了目前人工神经网络领域中常用的共轭梯度法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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