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首页> 外文期刊>International Journal of Neural Systems >Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features
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Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features

机译：基于使用高斯混合模型和灰度共同发生矩阵特征的EEG信号时频图像的癫痫癫痫检测

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摘要

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.
机译：脑电图（EEG）信号分析是评估神经系统疾病中的有价值的工具，其通常用于诊断癫痫发作。本文提出了一种用于癫痫癫痫发作检测的新型自动EEG信号分类方法。所提出的方法首先采用连续小波变换（CWT）方法来获得EEG信号的时频图像（TFI）。然后，处理的EEG信号被分解成临床关注的五个子带频率分量，因为这些子带频率分量表示更好的识别特性。然后从这些子带TFI中提取高斯混合模型（GMM）特征和灰度共发生矩阵（GLCM）描述符。另外，为了提高分类精度，采用紧凑型特征选择方法，通过组合Creieff和基于支持向量机的递归特征消除（RFE-SVM）算法来选择最辨别的特征子集，这是一个输入到的SVM具有径向基函数（RBF），用于分类癫痫癫痫发作脑电图。来自公开的基准数据库的实验结果表明，所提出的方法提供比文献中最近提出的方法更好的分类精度，表明所提出的方法在检测癫痫发作中的有效性。

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