Feature extraction of EEG signal using wavelet transform for autism classification
Feature extraction of EEG signal using wavelet transform for autism classification
复制标题
使用小波变换提取脑电信号特征进行自闭症分类
DOI：
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发表时间：
2015
影响因子：
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通讯作者：
S. S. Hussin
中科院分区：
文献类型：
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作者：
Chuin Cheong Lung;R. Sudirman;S. S. Hussin
特征提取是在进行分类之前从脑电信号（EEG）中提取信息以表示大数据集的过程。研究了离散小波变换在自闭症儿童感觉反应脑电信号特征提取中的应用。在这项研究中，离散小波变换被用来分解滤波后的脑电信号的频率分量和离散小波系数的统计特征的时域计算。这些特征用于训练多层感知器（MLP）神经网络，将信号分类为三种自闭症严重程度（轻度、中度和重度）。训练结果的分类准确率达到92.3%，均方误差为0.0362。对训练好的神经网络的测试表明，用于测试的所有样本都被正确分类。
Feature extraction is a process to extract information from the electroencephalogram (EEG) signal to represent the large dataset before performing classification. This paper is intended to study the use of discrete wavelet transform (DWT) in extracting feature from EEG signal obtained by sensory response from autism children. In this study, DWT is used to decompose a filtered EEG signal into its frequency components and the statistical feature of the DWT coefficient are computed in time domain. The features are used to train a multilayer perceptron (MLP) neural network to classify the signals into three classes of autism severity (mild, moderate and severe). The training results in classification accuracy achieved up to 92.3% with MSE of 0.0362. Testing on the trained neural network shows that all samples used for testing is being classified correctlyARPN Journal of Engineering and Applied Sciences