Liu Qi, Zhao Xiaoguang, Hou Zengguang, Liu Hongguang
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, CAS, Beijing, China.
Institute of Crime, Chinese People's Public Security University, Beijing, China.
Technol Health Care. 2017 Jul 20;25(S1):399-409. doi: 10.3233/THC-171343.
This paper presents a pattern recognition model using multiple features and the kernel extreme learning machine (ELM), improving the accuracy of automatic epilepsy diagnosis. After simple preprocessing, temporal- and wavelet-based features are extracted from epileptic EEG signals. A combined kernel-function-based ELM approach is then proposed for feature classification. To further reduce the computation, Cholesky decomposition is introduced during the process of calculating the output weights. The experimental results show that the proposed method can achieve satisfactory accuracy with less computation time.
本文提出了一种使用多种特征和核极限学习机（ELM）的模式识别模型，提高了癫痫自动诊断的准确率。经过简单预处理后，从癫痫脑电信号中提取基于时间和小波的特征。然后提出一种基于组合核函数的ELM方法进行特征分类。为了进一步减少计算量，在计算输出权重的过程中引入了Cholesky分解。实验结果表明，该方法能够以较少的计算时间实现令人满意的准确率。
