Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran.
Artif Intell Med. 2009 Nov;47(3):263-74. doi: 10.1016/j.artmed.2009.03.003. Epub 2009 Apr 29.
In this paper, electroencephalogram (EEG) signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of classifying the two groups.
For each case, 20 channels of EEG are recorded. Several features including Shannon entropy, spectral entropy, approximate entropy, Lempel-Ziv complexity and Higuchi fractal dimension are extracted from EEG signals. Leave-one (participant)-out cross-validation is used for reliable estimate of the separability of the two groups. The training set is used for training the two classifiers, namely, linear discriminant analysis (LDA) and adaptive boosting (Adaboost). Each classifier is assessed using the test dataset.
A classification accuracy of 86% and 90% is obtained by LDA and Adaboost respectively. For further improvement, genetic programming is employed to select the best features and remove the redundant ones. Applying the two classifiers to the reduced feature set, a classification accuracy of 89% and 91% is obtained by LDA and Adaboost respectively. The proposed technique is compared and contrasted with a recently reported method and it is demonstrated that a considerably enhanced performance is achieved.
This study shows that EEG signals can be a useful tool for discrimination of the schizophrenic and control participants. It is suggested that this analysis can be a complementary tool to help psychiatrists diagnosing schizophrenic patients.
本文分析了 20 名精神分裂症患者和 20 名年龄匹配的对照参与者的脑电图（EEG）信号，旨在对这两组进行分类。
对于每个病例，记录 20 个通道的 EEG。从 EEG 信号中提取了几个特征，包括香农熵、谱熵、近似熵、Lempel-Ziv 复杂度和 Higuchi 分形维数。采用留一（参与者）交叉验证法可靠估计两组的可分离性。训练集用于训练两个分类器，即线性判别分析（LDA）和自适应增强（Adaboost）。使用测试数据集评估每个分类器。
LDA 和 Adaboost 分别获得了 86%和 90%的分类准确率。为了进一步提高精度，采用遗传编程选择最佳特征并去除冗余特征。将这两个分类器应用于简化的特征集，LDA 和 Adaboost 分别获得了 89%和 91%的分类准确率。将所提出的技术与最近报道的方法进行比较和对比，结果表明，该方法的性能得到了显著提高。
本研究表明，脑电图信号可作为区分精神分裂症患者和对照组参与者的有用工具。建议该分析可作为帮助精神科医生诊断精神分裂症患者的辅助工具。
