模糊散布熵及其应用  

Fuzzy Dispersion Entropy and Its Application

作　　者：胡保华 朱宗俊 金飞翔 鲁翠萍 修磊 王勇[4] HU Baohua;ZHU Zongjun;JIN Feixiang;LU Cuiping;XIU Lei;WANG Yong(School of Advanced Manufacturing Engineering,Hefei University,Hefei 230601,China;Anhui Provincial Engineering Technology Research Center of Intelligent Vehicle Control and Integrated Design Technology,Hefei 230601,China;Department of Acupuncture and Rehabilitation,The First Affiliated Hospital of Anhui University of Chinese Medicine,Hefei 230031,China;School of Mechanical Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥学院先进制造工程学院,合肥230601 [2]安徽省智能车辆控制与集成设计技术工程研究中心,合肥230601 [3]安徽中医药大学第一附属医院针灸康复科,合肥230031 [4]合肥工业大学机械工程学院,合肥230009

出　　处：《计算机工程与应用》2024年第2期326-336,共11页Computer Engineering and Applications

基　　金：国家自然科学基金(U1713210);安徽省科技重大专项(202103a07020009);安徽省重点研究与开发计划项目(2022a05020043);合肥学院人才项目(21-22RC01)。

摘　　要：散布熵(dispersion entropy,DispEn)是近期提出的一种衡量信号不规则程度的动力学指标。相比于样本熵,散布熵可同时检测信号幅度和频率的变化,计算时间也大大缩短。然而,由于散布熵是基于取整函数(阶跃函数)设置的,对数据长度和参数选择较敏感,特别是类的数量(量化级别),某些情况下由噪声引起的信号幅值的微小变化会改变量化序列从而引起熵值剧烈波动。为解决这些局限性,结合模糊隶属度函数提出了模糊散布熵(fuzzy dispersion entropy,FuzzyDispEn)。在FuzzyDispEn中,基于欧式距离实现嵌入向量与量化级别的模糊化隶属。使用合成时间序列信号测试FuzzyDispEn相比DispEn的性能。结果表明,与DispEn相比,FuzzyDispEn对信号数据长度、参数选择的灵敏度更低,而且抗噪性更好。FuzzyDispEn还应用于脑电与轴承信号的复杂度检测,实验结果表明在真实物理信号分析方面FuzzyDispEn的性能表现同样优于DispEn。结果表明模糊散布熵可为信号复杂度测量提供一种新的方法。Dispersion entropy(DispEn)is a new dynamic index to measure the degree of signal irregularity.Compared with sample entropy,dispersion entropy can detect both amplitude and frequency changes at the same time and shorten the calculation time greatly.However,DispEn is sensitive to parameter selection,especially the number of classes(quanti-zation level).Since the dispersion entropy is set based on the round function(step function),in some cases a small change in signal amplitude due to noise will alter the quantization sequence,thus changing the entropy value.In order to solve these limitations,fuzzy dispersion entropy(FuzzyDispEn)is proposed by combining fuzzy membership function and dis-persion entropy.In FuzzyDispEn,fuzzy membership between embedding vector and quantization level is realized based on Euclidean distance.The advantages of FuzzyDispEn are tested using different synthetic time series signals.The results show that compared with DispEn,FuzzyDispEn has lower sensitivity to signal length and parameter selection,and better anti-noise performance.FuzzyDispEn is also used in completely analysis of EEG and bearing signal.Experimental results show that FuzzyDispEn also performs better than DispEn in real physical signal analysis.The results show that fuzzy dis-persion entropy can provide a new method for signal complexity measurement.

关 键 词：模糊散布熵 模糊隶属度函数 复杂性 脑电信号 轴承信号 

分 类 号：R318[医药卫生—生物医学工程] TP391[医药卫生—基础医学]