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- 2017
基于EMD和SVM的抑郁症静息态脑电信号分类研究
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Abstract:
摘要： 以静息态脑电信号为基础,通过固有模态分解(empirical mode decomposition, EMD)算法对脑电信号进行信号去噪和特征值提取,通过支持向量机(support vector machine, SVM)算法对抑郁症患者和正常对照组人群的脑电特征值进行分类分析。 通过系统化的数据采集试验,采集了20位抑郁症患者和25位健康对照组的静息态脑电信号;对静息态脑电信号进行信号的去噪和特征提取;采用SVM算法对抑郁症患者和正常人对照组脑电特征值进行二值分类,分类正确率达到93.3%。 相较于传统的小波变换提取的特征值,分类准确率有明显的提高。
Abstract: Automatic detection of depression state was significant for mental disease diagnostics and rehabilitation, which could decrease the duration of work required when inspecting the electroencephalography(EEG)signals. A novel method for feature extraction and pattern recognition from subjects resting state EEG signal, based upon empirical mode decomposition(EMD)and support vector machine(SVM)was proposed to make a distinction between depression patients and normal controls. The EEG signals were collected from 20 depression patients and 25 normal persons, and the EEG was filtered and extracted as features. The SVM was used as classifier for recognition which showed whether the person was a depression patient. The experimental results showed that the algorithm could achieve the specificity of 93.3%. And the classification accuracy from the features extracted by EMD was higher than the classification accuracy from features extracted by wavelet clearly
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