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首页> 外文期刊>Clinical EEG and neuroscience: official journal of the EEG and Clinical Neuroscience Society (ENCS) >Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach
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Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach

机译：脑电信号的特征选择和分类：基于人工神经网络和遗传算法的方法

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摘要

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.
机译：特征选择是许多模式识别系统中的重要步骤，旨在克服所谓的维数诅咒。在这项研究中，优化的分类方法在147例经反复经颅磁刺激（rTMS）治疗的重度抑郁症（MDD）患者中进行了测试。使用theta和delta频段的6通道pre-rTMS脑电图（EEG）模式，评估了遗传算法（GA）和反向传播（BP）神经网络（BPNN）组合的性能。遗传算法首先用于消除冗余和较少歧视的功能，以最大化分类性能。然后将BPNN应用于测试特征子集的性能。最后，使用6倍交叉验证评估使用该子集的分类性能。尽管额电极的慢带被广泛用于收集MDD患者的EEG数据并提供了令人满意的分类结果，但该方法的结果表明总体准确度显着提高了89.12％，并且接收器的工作特性（ROC）范围以下）曲线（AUC）为0.904（使用精简特征集）。

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