摘要
准确区分生理序列随机性与混沌性,且不受序列长度与参数的影响是衡量复杂度算法的关键。本文提出了一种编码式Lempel-Ziv(LZ)算法,分别从序列随机性与混沌性的区分、长度的影响、动力学性质突变的敏感性、高斯白与粉红噪声复杂度测量等4个方面与经典LZ算法、多状态LZ算法、样本熵以及排列熵进行比较。结果显示,在短、中、长时(100、500、5 000点)下,编码式LZ算法均能准确区分随机与混沌性,正确测度高斯噪声的复杂度低于粉红噪声,并能准确响应序列动力学性质的改变。本文采用美国麻省理工学院(MIT)和波士顿贝斯以色列医院(BIH)联合建立的的MIT-BIH心电数据库中的充血性心力衰竭RR间期(CHF-RR)数据和正常窦性心律RR间期(NSR-RR)数据进行测试,实验结果显示,在各种时长下,编码式LZ复杂度算法均能准确地得出心力衰竭的复杂度低于窦性心律(P<0.01)的结果,且不受长度与参数影响,具有较强的泛化能力。
To distinguish the randomness and chaos characteristics of physiological signals and to keep its performance independent of the signal length and parameters are the key judgement of performance of a complexity algorithm. We proposed an encoding Lempel-Ziv （LZ） complexity algorithm to try to explicitly discern between the randomness and chaos characteristics of signals. Our study also compared the effects of length of time series, the sensitivity to dynamical properties change of time series and quantifying the complexity between gauss noise and 1/f pink noise ELZ with those from classic LZ （CLZ）, multi-state LZ （MLZ）, sample entropy （SampEn） and permutation entropy （PE）. The experimental results showed ELZ could not only distinguish the randomness and chaos characteristics of time series on all time length （i.e. 100, 500, 5 000）, but also reflected exactly that the complexity of gauss noise was lower than that of pink noise, and responded change of dynamic characteristics of time series in time. The congestive heart failure （CHF） RR Interval database and the normal sinus rhythm （NSR） RR Interval database created by Massachusetts Institute of Technology （MIT） and Boston Beth Israel Hospital（BIH）were used as real data in our study. The results revealed that the ELZ could show the complexity of congestive heart failure which was lower than that of normal sinus rhythm during all lengths of time series （P〈0.01）, and the ELZ algorithm had better generalization ability and was independent of length of time series.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2016年第6期1176-1182,1190,共8页
Journal of Biomedical Engineering
基金
国家自然科学基金面上资助项目(61473174)
山东省优秀中青年科学家科研奖励基金资助项目(BS2013DX029)
中国博士后科学基金资助项目(2013M530323)
