关键词: 脑电图, 脑卒中, &alpha, 波段, 小波包能量, 近似熵

Abstract: In order to effectively classify and detect electroencephalogram signals in stroke patients with midbrain infarction and cerebral hemorrhage, the article proposes an automatic classification prediction method. The method bases on the feature fusion of wavelet packet energy and approximate entropy feature. Firstly, the input signal is decomposed for getting the energy of each frequency bands, then the energy dimension is reduced. After that the energy after dimension reduction is fused with the signal’s approximate entropy to get the final features. Lastly, support vector machine algorithm is used to train the prediction model for classifying the stroke input data. The research results show that this method has good ability of EEG feature signal classification. Moreover, extracting alpha band EEG signal alone as input signal, the experimental results show that especially the high rate can be obtained. The prediction accuracy of cerebral infarction and cerebral hemorrhage can reach 98.36% in average. It plays a good auxiliary decision-making role in the clinical prediction and detection of stroke diseases.

Key words: electroencephalogram, stroke, alpha band, wavelet packet energy, approximate entropy