| Synthesized Multi-Method to Detect and Classify Epileptic Waves in EEG |
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| 作者姓名： | 万柏坤 毕卡诗 綦宏志 赵丽 |
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| 作者单位： | SchoolofPrecisionInstrumentsandOpto-ElectronicsEngineering,TianjinUniversity,Tianjin300072,China |
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| 基金项目： | SupportedbyNationalNaturalScienceFoundationofChina(No .60 4 71 0 2 8)，TianjinNaturalScienceFoundation(No .993 60 751 1 )andTianjinKeySubjectFund(No .2 0 0 0 3 1 ) |
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| 摘 要： | In order to sufficiently exploit the advantages of different signal processing methods, such as wavelet transformation (WT), artificial neural networks (ANN) and expert rules (ER), a synthesized multi-method was introduced to detect and classify the epileptic waves in the EEG data. Using this method, at first, the epileptic waves were detected from pre-processed EEG data at different scales by WT, then the characteristic parameters of the chosen candidates of epileptic waves were extracted and sent into the well-trained ANN to identify and classify the true epileptic waves,and at last, the detected epileptic waves were certificated by ER. The statistic results of detection and classification show that, the synthesized multi-method has a good capacity to extract signal features and to shield the signals from the random noise. This method is especially fit for the analysis of the biomedical signals in biomedical engineering which are usually non-placid and nonlinear.
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| 关 键 词： | 信号处理 小波变换 癫痫波 人工神经网络 专家系统 EEG数据 |
Synthesized Multi-Method to Detect and Classify Epileptic Waves in EEG |
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| WAN Bai-kun,DHAKAL Bikash,QI Hong-zhi,ZHAO Li.Synthesized Multi-Method to Detect and Classify Epileptic Waves in EEG[J].Transactions of Tianjin University,2004,10(4):247-251. |
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| Authors: | WAN Bai-kun DHAKAL Bikash QI Hong-zhi ZHAO Li |
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| Abstract: | In order to sufficiently exploit the advantages of different signal processing methods, such as wavelet transformation (WT), artificial neural networks (ANN) and expert rules (ER),a synthesized multi-method was introduced to detect and classify the epileptic waves in the EEG data. Using this method, at first, the epileptic waves were detected from pre-processed EEG data at different scales by WT, then the characteristic parameters of the chosen candidates of epileptic waves were extracted and sent into the well-trained ANN to identify and classify the true epileptic waves,and at last, the detected epileptic waves were certificated by ER. The statistic results of detection and classification show that, the synthesized multi-method has a good capacity to extract signal features and to shield the signals from the random noise. This method is especially fit for the analysis of the biomedical signals in biomedical engineering which are usually non-placid and nonlinear. |
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| Keywords: | epileptic EEG wave wavelet transformation(WT) artificial neural network(ANN) expert rule(ER) |
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