北京生物医学工程

心律失常的多重分形去趋势波动分析

Multifractal detrended fluctuation analysis for the pathological electrocardiograph signals

作者：赵烨南王俊

单位：南京邮电大学通信与信息工程学院图像处理与图像通信江苏省重点实验室（南京210003）

分类号：

出版年·卷·期（页码）：2012·31·5（474-477）

摘要：

目的心律失常的及早诊断对及时救助病人具有重要意义。方法基于多重分形去趋势波动分析方法，本文对正常心电信号及窦、房性心律失常信号进行分析。结果发现三种信号都具有不同程度的长程相关性和多重分形特性，且在波动函数的阶数为正值时，三种信号的长程相关特性最为明显。通过比较三种信号的多重分形谱，发现正常心电信号的多重分形谱宽度最小，窦性心动过缓信号次之，心房颤动宽度最大。结论此研究结果对临床医学诊断区分心律失常与正常心电信号有很好的借鉴意义。

Objective The early diagnosis and timely salvation for patients with arrhythmia is of great significance. Methods In this paper, multifractal detrended fluctuation analysis method was used to analyze the multifractal characteristics of the sinus bradycardia（SBR）signals, the atrial fibrillation(AF) signals and the normal electrocardiograph (ECG) signals. Results We found that the three kinds of signals were long-range correlating and multifractal. When the order of fluctuation function was positive, the three kinds of signals showed distinct long-range correlation properties. To compare with the multifractal spectra of the three signals, the width of the normal ECG signals’ spectrum was the smallest, the SBR signals’ was bigger and the width of the AF signals’ was the biggest. Conclusions This study provided good reference for clinical diagnosis of SBR and AF signals.

参考文献：

［1］杨钧国，李治安. 现代心电图学［M］. 北京：科学出版社，1997：1-1036.
［2］宋智，李焱淼.时频分析在心电信号分析中的应用［J］.大连交通大学学报，2010，12(02)：56-59.
Song Zhi, Li Yanmiao. Study of time-frequency analysis and application in ECG analysis［J］. Journal of Dalian Jiaotong University，2010，12(02)：56-59.
［3］Buldyrev SV, Goldberger AL, Havlins, et al. Long-range correlation properties of coding and noncoding DNA sequences: GenBank analysis ［J］. Phys Rev E, 1995, 51(5):5084-5091.
［4］杨毓英, 史习智, 蔡悦斌.基于黑板模型的心电信号解释系统原型BBIS-ECG原型设计［J］. 北京生物医学工程，1996，(4)：193-197.
Yang Yuying, Shi Xizhi, Cai Yuebing. Design of BBIS-ECG—a blackboard based interpretation system for ECG signal［J］. Beijing Biomedical Engineering, 1996，(4)：193-197.
［5］Iyengar N, Peng CK, Morin R, et al. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics ［J］. Am J Physiol Regul Integr Comp Physiol, 1996, 271:1078-1084.
［6］Tulppo MP, Hughson RL, Makikallio TH, et al. Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics ［J］. Am J Physiol Heart Circ Physiol, 2001, 280(3): H1081- H1088.
［7］Peitgen HO, Jurgens H, Saupe D. Chaos and Fractals: New Frontiers of Science［M］. New York，Springer, 1992：183-228.
［8］Bacry E, Delour J, Muzy JF. Multifractal random walks［J］. Phys Rev E, 2001, 64: 026103-026105.
［9］Muzy JF, Bacry E, Arneodo A. Multifractal formalism for fractal signal: The structure function approach versus the wavelet-transform modulus-maxima method［J］. Phys Rev E, 1993, 47: 875-883.
［10］Peng CK, Buldyrev SV, Havlin S, et al. Mosaic organization of DNA nucleotides［J］. Phys Rev E, 1994, 49(2): 1685-1389.
［11］Kantelhardt JW, Zschiegner SA, Koscielny-bunde E, et al. Multifractal detrended fluctuation analysis of nonstationary time series［J］. Physica A, 2002, 316 (1- 4): 87-114.
［12］马斌荣，陈卉. 医学科研中的统计方法［M］. 北京: 科学出版社，2005：33-40.
［13］谢瑛瑛，诸强. 心率变异性的检测与分析［J］. 北京生物医学工程， 2010， 29（2）：215-219.
Xie Yingying, Zhu Qiang. Measurement and analysis of heart rate vartabi Lity［J］. Beijing Biomedical Engineering ,2010, 29(2):215-219.

服务与反馈：

【文章下载】【加入收藏】

提示：您还未登录，请登录！点此登录