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机译:基于小波的非线性特征和极限学习机的癫痫发作检测框架
Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China,Department of Automation, East China University of Science and Technology. 130 Meilong Road, Shanghai 200237, PR China;
Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China;
Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China;
Department of Neurosurgery, Renji Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200233, PR China;
Department of Neurosurgery, Renji Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200233, PR China;
Seizure detection; Wavelet decomposition; Approximate entropy (ApEn); Sample entropy (SampEn); Recurrence quantification analysis (RQA); Extreme learning machine (ELM); Support vector machine (SVM);
机译:癫痫发作检测的新方法:基于样本熵的特征提取和极限学习机
机译:使用乘积极限学习机的连续方法用于癫痫发作的检测
机译:一种优化的极限学习机,用于癫痫发作检测
机译:基于熵和极限学习机的癫痫发作自动检测
机译:一种机器学习框架,用于模拟非线性海洋动力学的极端事件
机译:通过使用先进的参数优化方法使用强大的机器学习分类技术以不同的特征提取策略检测癫痫发作
机译:癫痫发作检测的新方法:基于样本熵的特征提取和极限学习机