计算机工程与应用2019，Vol.55Issue(24)：154-158,258,6.DOI:10.3778/j.issn.1002-8331.1808-0303
面向特征融合的脑卒中脑电信号分类方法
Classification of Stroke EEG Signals Based on Feature Fusion
摘要
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.关键词
脑电图/脑卒中/α波段/小波包能量/近似熵Key words
electroencephalogram/stroke/alpha band/wavelet packet energy/approximate entropy分类
信息技术与安全科学引用本文复制引用
王灿,李凤莲,胡风云,张雪英,贾文辉..面向特征融合的脑卒中脑电信号分类方法[J].计算机工程与应用,2019,55(24):154-158,258,6.基金项目
山西省重点研发社发项目(No.201803D31045) （No.201803D31045）
山西省自然科学基金(No.201801D121138) （No.201801D121138）
山西省研究生教育创新计划(No.2018BY051,No.2018SY023,No.2018SY024) （No.2018BY051,No.2018SY023,No.2018SY024）
山西省研究生联合培养基地人才培养项目(No.2017JD16) （No.2017JD16）
国家自然科学基金(No.61376693) （No.61376693）
山西省优秀人才科技创新项目(No.201605D211021) （No.201605D211021）
山西省重点研发计划(No.201603D321060) （No.201603D321060）
吴阶平基金科研项目(No.320675016129). （No.320675016129）
