基于熵的严重意识障碍脑电信号识别方法

An Assessment Method of Electroencephalograph Signals in Severe Disorders of Consciousness Based on Entropy

本研究旨在实现对植物状态和最小意识状态脑电信号的分类识别。通过对植物状态和最小意识状态患者施加唤名刺激,采集被唤名时患者的脑电信号;然后对脑电数据进行去噪预处理、样本熵和多尺度熵的特征提取;最后将提取的数据特征送入多核学习支持向量机(SVM)中进行训练和分类。试验结果表明,严重意识障碍患者alpha波脑电特征表现显著,平均分类精度为88.24%,实现了定量化的严重意识障碍状态判定,为意识障碍程度的临床诊断提供了辅助依据。

This paper explores a methodology used to discriminate the electroencephalograph （EEG） signals of patients with vegetative state （VS） and those with minimally conscious state （MCS）. The model was derived from the EEG data of 33 patients in a calling name stimulation paradigm. The preprocessing algorithm was applied to remove the noises in the EEG data. Two types of features including sample entropy and multiscale entropy were chosen. Multiple kernel support vector machine was investigated to perform the training and classification. The experimental results showed that the alpha rhythm features of EEG signals in severe disorders of consciousness were significant. We achieved the average classification accuracy of 88.24%. It was concluded that the proposed method for the EEG signal classification for VS and MCS patients was effective. The approach in this study may eventually lead to a reliable tool for identifying severe disorder states of consciousness quantitatively. It would also provide the auxiliary basis of clinical assessment for the consciousness disorder degree.