关键词: 脑卒中, 集成分类器, 定量脑电图特征, 脑网络, 意识障碍, 机器学习

Abstract:

The detection of Disorders of Consciousness（DoC） based on clinical experiments is time and manpower consuming and these experiments are intervalic. The Electroencephalogram（EEG） of stroke patients is collected to study the automatic classification of stroke patients with DoC and without DoC. As many as 9 kinds of quantitative EEG features are extracted to build brain network, and then the connectivity features of the brain network are imported into classifiers to classify DoC and no DoC in stroke patients. An Ensemble Of Support Vector Machine（EOSVM） is designed to solve the problem that classifiers always tend to the majority classes in the classification on imbalanced dataset. The experimental results show that the existing classifiers can classify the stroke patients with DoC and without DoC with an accuracy of about 70% and a sensitivity lower than 40%, however, the EOSVM classifier gives an accuracy of 96.79%, the sensitivity of 95.45% and specificity of 100%. These results show that the classifier EOSVM can improve the classification of imbalanced EEG dataset, and help the automatic detection of DoC in stroke patients.

Key words: stroke patients, ensemble classifier, quantitative EEG, brain network, disorders of consciousness, machine learning