摘要
为进一步提高互模糊熵(cross fuzzy entropy,X-FuzzyEn)算法的统计性能,引入了调整因子λ,定义了一类新的模糊隶属函数,提出了改进X-FuzzyEn算法。使用耦合噪声模型和耦合MIX(p)模型定量评价了改进算法的统计稳定性和相对一致性;通过实际心衰患者和健康志愿者之间的心动周期和舒张间期耦合分析,对改进算法的有效性进行了验证。结果表明,改进算法的统计性能显著提升,并可以有效区分心衰患者与健康志愿者。
To further improve the statistical performances of cross fuzzy entropy( X-FuzzyEn) algorithm,an adjustable factor λ w as introduced and a refined X-FuzzyEn method w as developed accordingly. Its statistical stability and relative consistency w as tested by coupled noise and coupled MIX( p) models. Then it w as validated by the coupling analysis of heart rate and cardiac diastolic period series betw een heart failure patients and healthy subjects. Results indicated that the refined algorithm had significantly improved performances and it w as capable to tell the differences betw een heart failure patients and healthy subjects.
引文
[1]GOLDBERGER A L.Non-linear dynamics for clinicians:chaos theory,fractals,and complexity at the bedside[J].Lancet,1996,347(9011):1312-1314.
[2]刘澄玉,杨静,李斌,等.基于LZ复杂度的脉搏波传播时间变异性分析[J].山东大学学报:工学版,2009,39(6):58-62.LIU Chengyu,YANG Jing,LI Bin,et al.Analysis of pulse transit time variability based on LZ complexity[J].Journal of Shandong University:Engineering Science,2009,36(9):58-62.
[3]刘澄玉,赵莉娜.熵理论发展史及其在生物医学信号分析中的作用[J].北京生物医学工程,2012,31(5):539-543.LIU Chengyu,ZHAO Lina.History of entropy theory and its role in biomedical signal analysis[J].Beijing Biomedical Engineering,2012,31(5):539-543.
[4]郭兴明,胡童宜,汤丽平.心脏杂音提取和分类识别研究[J].计算机工程与应用,2012,48(15):149-152.GUO Xingming,HU Tongyi,TANG Liping.Heart murmur extraction from heart sound and classification[J].Computer Engineering and Applications,2012,48(15):149-152.
[5]HU Z,SHI P.Interregional Functional Connectivity via Pattern Synchrony[C]//9th International Conference on Control,Automation,Robotics and Vision.Singapore:IEEE,2006:1-6.
[6]张立伟,刘国忠,罗倩,等.视听刺激脑电信号的相位同步分析[J].生物医学工程学杂志,2012,29(4):645-649.ZHANG Liw ei,LIU Guozhong,LUO Qiang,et al.Phase synchronization analysis of EEG signal during audio-visual stimulation[J].Journal of Biomedical Engineering,2012,29(4):645-649.
[7]PINCUS S M.Approximate entropy as a measure of system complexity[J].Proceedings of the National Academy of Sciences,1991,88(6):2297-2301.
[8]RICHMAN J S,MOORMAN J R.Physiological time-series analysis using approximate entropy and sample entropy[J].American Journal of Physiology-Heart and Circulatory Physiology,2000,278(6):H2039-H2049.
[9]ZHANG T,YANG Z,COOTE J H.Cross-sample entropy statistic as a measure of complexity and regularity of renal sympathetic nerve activity in the rat[J].Experimental Physiology,2007,92(4):659-669.
[10]XIE H,GUO J,ZHENG Y.A comparative study of pattern synchronization detection between neural signals using different cross-entropy measures[J].Biological Cybernetics,2010,102(2):123-135.
[11]XIE H,ZHENG Y,GUO J,et al.Cross-fuzzy entropy:A new method to test pattern synchrony of bivariate time series[J].Information Sciences,2010,180(9):1715-1724.
[12]LI P,LIU C,WANG X,et al.Testing pattern synchronization in coupled systems through different entropy-based measures[J].Medical&Biological Engineering&Computing,2013,51(5):581-591.
[13]李鹏,刘澄玉,李丽萍,等.多尺度多变量模糊熵分析[J].物理学报,2013,62(12):120512.LI Peng,LIU Chengyu,LI Liping,et al.Multiscale multivariate fuzzy entropy analysis[J].Acta Physica Sinica,2013,62(12):120512-120512-9.
[14]LI P,LIU C,WANG X,et al.A low-complexity data-adaptive approach for premature ventricular contraction recognition[J/OL].Signal,Image and Video Processing,2013.doi:10.1007/s11760-013-0478-6.
[15]LI L,LIU C,LI K,et al.Comparison of detrending methods in frequency domain analysis of R-R interval series[C]//4th International Conference on Measuring Technology and Mechatronics Automation.Sanya,China:ICMTMA,2012:1359-1362.