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机译:使用小波分组分解和局部减去波动分析准确分类癫痫癫痫发作类型
Zhejiang Univ Inst VLSI Design Hangzhou 310027 Peoples R China;
Zhejiang Univ Inst VLSI Design Hangzhou 310027 Peoples R China;
Zhejiang Univ Inst VLSI Design Hangzhou 310027 Peoples R China;
signal classification; electroencephalography; support vector machines; fractals; wavelet transforms; medical signal processing; combined fractal spectrum features; total classification accuracy; epilepsy seizure types; wavelet packet decomposition; local detrended fluctuation analysis; electroencephalogram signals; vital information; visual inspection; novel classification method; WPD; L-DFA; computer-aided diagnostic system; raw EEG signals; intrinsic frequency bands; sub-band signals;
机译:离散小波包变换与使用定制母小波的去趋势波动分析相结合,目的是为外骨骼提供图像-电机控制界面
机译:离散小波变换与小波包分解的肺声分类比较
机译:使用短期ECG信号的小波包分析对睡眠呼吸暂停类型进行分类。
机译:使用经验模态分解和趋势波动分析的癫痫检测
机译:癫痫癫痫发作症的侧向化和定位特征选择与分类方法
机译:利用小波包对数能量和范数熵和递归Elman神经网络分类器对癫痫发作进行分类
机译:使用小波包分解和倒谱分析对基于脑电图的脑计算机接口进行单次试验分类