Classification of EEG signals using the wavelet transform
Classification of EEG signals using the wavelet transform
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DOI：
10.1016/s0165-1684(97)00038-8
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发表时间：
1997-05-01
影响因子：
4.4
通讯作者：
Sergejew, A
中科院分区：
文献类型：
--
作者：
Hazarika, N;Chen, JZ;Sergejew, A
脑电图（EEG）被广泛用于临床研究大脑疾病。然而，在严重的精神疾病的脑电图异常是有时太微妙的检测使用传统的技术。本文描述了人工神经网络（ANN）技术与特征提取技术的应用，即，小波变换，用于脑电信号的分类。利用小波变换对信号进行数据约简和预处理。使用三类EEG信号：正常、精神分裂症（SCH）和强迫症（OCD）。在分类中使用的人工神经网络的结构是一个三层前馈网络，实现误差学习算法的反向传播。训练后，小波系数的网络能够正确分类超过66%的正常类和71%的精神分裂症类的脑电图，分别。因此，小波变换提供了一个潜在的强大的技术，用于预处理EEG信号之前的分类。(C)1997年Elsevier Science B.V.
The electroencephalogram (EEG) is widely used clinically to investigate brain disorders. However, abnormalities in the EEG in serious psychiatric disorders are at times too subtle to be detected using conventional techniques. This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. The data reduction and preprocessing operations of signals are performed using the wavelet transform. Three classes of EEG signals were used: Normal, Schizophrenia (SCH), and Obsessive Compulsive Disorder (OCD). The architecture of the artificial neural network used in the classification is a three-layered feedforward network which implements the backpropagation of error learning algorithm. After training, the network with wavelet coefficients was able to correctly classify over 66% of the normal class and 71% of the schizophrenia class of EEGs, respectively. The wavelet transform thus provides a potentially powerful technique for preprocessing EEG signals prior to classification. (C) 1997 Elsevier Science B.V.