计算机工程2017，Vol.43Issue(6)：53-58,6.DOI:10.3969/j.issn.1000-3428.2017.06.009
联合改进CEEMD与近似熵的脑电去噪方法
Electroencephalogram Denoising Method Combining Improved CEEMD and Approximate Entropy
摘要
Abstract
Aiming at the problem of modal selection bias in Complete Ensemble Empirical Mode Decomposition (CEEMD),this paper proposes a new Electroencephalogram (EEG) signal denoising method by combining improved CEEMD (ICEEMD).First,the EEG signal is decomposed to several Intrinsic Mode Functions(IMF) by ICEEMD.Then,the approximate entropy of each IMF is calculated respectively.Finally,the IMF with the maximum approximate entropy is chosen as the denoised result.The experiments result based on analog signals and real EEG signals shows that,compared with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(ECCMDAN),the new method can give more clear and stable denoising results,and it also solves the problems such as inaccurate denoising and false mode caused by the blind selection of IMF.关键词
脑电/去噪/本征模态函数/完备总体经验模态分解/近似熵Key words
Electroencephalogram (EEG)/denoising/Intrinsic Mode Function (IMF)/Complete Ensemble Empirical Mode Decomposition(CEEMD)/approximate entropy分类
信息技术与安全科学引用本文复制引用
张欢,刘燕,佟宝同,赵凌霄,杨莹雪,王玉平,戴亚康..联合改进CEEMD与近似熵的脑电去噪方法[J].计算机工程,2017,43(6):53-58,6.基金项目
国家“863”计划项目(2015AA020514) （2015AA020514）
国家自然科学基金(61301042) （61301042）
中国科学院百人计划项目 （）
江苏省自然科学基金(BK2012189) （BK2012189）
苏州市医疗器械与新医药专项(ZXY201426) （ZXY201426）
2014年度中法“蔡元培”交流合作项目(201404490123) （201404490123）
脑功能疾病调控治疗北京市重点实验室开放课题. （）
