A Robust Extraction Algorithm Based on ICA Neural Network

摘要：

Independent component analysis (ICA), blind source separation(BSS) and related methods like blind source extraction (BSE) are all the promising unsupervised neural net-work technique for analysis of biomedical signals, especially for ECG, EEG and fMRI data. However, most of source extraction algorithms based on ICA neural network are not suitable to extract the desired signal since these algorithms are not to obtain the desired signal as the first output signal. In this paper, we propose an algorithm based on ICA neural network that can extract a desired source signal as the first output signal with a given kurtosis range. Because of adopting a robust objective function, the algorithm becomes very robust to outliers and spiky noise. Simulations on artificially generated data and real-world ECG data have shown that the algorithm can achieve satisfying results.

作者: Yalan Ye Zhi-Lin Zhang Quanyi Mo Jiazhi Zeng

作者单位: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.

会议类型: 国际会议

会议名称: 2007年通信、电路与系统国际会议(2007 International Conference on Communications,Circuits and Systems Proceedings)

会议地点: 日本福冈

会议语种:英文

在线出版日期: 2007-07-11（万方平台首次上网日期，不代表论文的发表时间）