文献详情
Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis
文献类型期刊
作者Wang, Lina[1];Xue, Weining[2];Li, Yang[3];Luo, Meilin[4];Huang, Jie[5];Cui, Weigang[6];Huang, Chao[7]
机构
通讯作者Li, Y (reprint author), Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China.; Li, Y (reprint author), Minjiang Univ, Fujian Prov Key Lab Informat Proc & Intelligent C, Fuzhou 350121, Peoples R China.
来源信息年:2017  卷:19  期:6  
期刊信息ENTROPY影响因子和分区  ISSN:1099-4300
关键词EEG; epileptic seizure detection; wavelet threshold denoising; wavelet feature extraction; nonlinear analysis; principal component analysis (PCA); analysis of variance (ANOVA)
摘要Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.
收录情况SCIE(WOS:000404454500001)  
所属部门自动化科学与电气工程学院
DOI10.3390/e19060222
学科物理:综合
人气指数18
浏览次数18
基金National Natural Science Foundation of China [61671042, 61403016]; Beijing Natural Science Foundation [4172037]; Fujian Provincial Key Laboratory in Minjiang University [MJUKF201702]; Specialized Research Fund for the Doctoral Program of Higher Education [20131102120008]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry; Fundamental Research Funds for the Central Universities
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