应用支持向量机方法对强迫症患者脑电图同步值进行分类

O. Tan, Mehmet Akif Özçoban, S. Aydın
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引用次数: 0

摘要

强迫症会对大脑振荡产生破坏性影响。这种破坏性影响之一是失去同步。全局场同步方法计算的全局场同步指数可以检测脑电的同步程度。分析结果显示,强迫症患者的全局场同步指数与健康组在theta和delta频带上存在显著差异。为了检验GFS方法检测强迫症的成功率,采用支持向量机方法对强迫症患者和健康组的GFS值进行分类。为了提高分类模型的性能,采用交叉验证方法选择训练数据和测试数据。δ波段的分类准确率为94.75%,θ波段的分类准确率为78.048%。该系统可以帮助医生诊断强迫症。分类结果表明,GFS是一种成功的强迫症诊断方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of EEG synchronization values of obsessive compulsive disorders patients using Support Vector Machine Method
Obsessive Compulsive Disorders causes disruptive effect on brain oscillations. One of this disruptive effects is loss of synchronization. Global Field Synchronization indice that is calculated by Global Field Synchronization Method can detect degree of synchronization of EEG. According to analysis results, significantly difference was found between Global Field Synchronization Indice of OCD patients and healthy group in theta and delta frequency bands. For the purpose of testing success of GFS method in detecting OCD, GFS values of OCD patients and healthy group classified with Support Vector Machine method. In order to increase the performance of classification model, training and test data was selected by Cross Validation Method. Accuracy rate of classification results was found at 94.75 in delta band and 78.048 percent in theta band. The system can assist the physicians for diagnosing OCD. The classification results has shown that GFS is a successful method for to diagnose OCD.
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