Complexity Analysis of Physiological Signals Using Encoding Lempel-Ziv Algorithm_Journal of Biomedical Engineering

Authors：

ZHANGYatao ^1,2 , LIUChengyu ¹ , LIUHai ² ,  WEIShoushui ¹

1. School of Control Science and Engineering, Shandong University, Jinan 250061, China;
2. School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China;

Corresponding author：

WEIShoushui, Email: sswei@sdu.edu.cn

Keywords：

encoding Lempel-Ziv algorithm; complexity; Logistic mapping; entropy; chaos

DOI：

10.7507/1001-5515.20160186

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： ZHANGYatao, LIUChengyu, LIUHai, WEIShoushui. Complexity Analysis of Physiological Signals Using Encoding Lempel-Ziv Algorithm. Journal of Biomedical Engineering, 2016, 33(6): 1176-1182. doi: 10.7507/1001-5515.20160186 Copy

1.	姚文坡, 刘铁兵,戴加飞,等.脑电信号的多尺度排列熵分析[J].物理学报,2014,63(7):078701-078704.
2.	LEMPEL A, ZIV J. On the complexity of finite sequences[J]. IEEE Transactions on Information Theory, 1976, 22(1): 75-81.
3.	SARLABOUS L, TORRES A, FIZ J A, et al. Multistate Lempel-Ziv (MLZ) Index Interpretation as a Measure of Amplitude and Complexity Changes [M]. 31st Annual International Conference of the IEEE EMBS. Minneapolis, Minnesota, USA. 2009: 4375-4378.
4.	SARLABOUS L, TORRES A, FIZ J A, et al. Index for estimation of muscle force from mechanomyography based on the Lempel-Ziv algorithm[J]. Journal of Electromyography and Kinesiology, 2013, 23(3): 548-557.
5.	ABOY M, HORNERO R, ABASOLO D, et al..interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis[J]. IEEE Trans Biomed Eng, 2006, 53(11): 2282-2288.
6.	RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
7.	黄晓林, 霍铖宇,司峻峰,等.等概率符号化样本熵应用于脑电分析[J].物理学报,2014,63(10):100501-100503.
8.	KALPAKIS K, YANG S, HU P F, et al. Permutation entropy analysis of vital signs data for outcome prediction of patients with severe traumatic brain injury[J]. Comput Biol Med, 2015(56): 167-174.
9.	ZANIN M, ZUNINO L, ROSSO O A, et al. Permutation entropy and its main biomedical and econophysics applications: a review[J]. Entropy, 2012, 14(8): 1553-1577.
10.	ZHANG Y T, WEI S S, LONG Y T, et al. Performance analysis of multiscale entropy for the assessment of ECG signal quality[J]. Journal of Electrical and Computer Engineering, 2015: 1-6.
11.	COSTA M, GOLDBERGER A L, PENG C K. Multiscale entropy analysis of biological signals[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2005, 71(2 Pt 1): 021906.
12.	ZHANG Xu, CHEN Xiang, BARKHAUS P E, et al. Multiscale entropy analysis of different spontaneous motor unit discharge patterns[J]. IEEE J Biomed Health Inform, 2013, 17(2): 470-476.
13.	ABÁSOLO D, ALCARAZ R, RIETA J J, et al. Lempel-Ziv complexity analysis for the evaluation of atrial fibrillation organization; proceedings of the 8th IASTED International Conference on Biomedical Engineering[C].Innsbruck, Austria, 2011: 1-7.
14.	ABÁSOLO D, HORNERO R, G M G, et al. Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure[J]. Med Eng Phys, 2006, 28(4): 315-322.
15.	RIEDL M, M L M, WESSEL N. Practical considerations of permutation entropy: A tutorial review[J]. Eur Phys J Spec Top, 2013, 222(2): 249-262.
16.	ZHAO Lina, WEI Shoushui, ZHANG Chengqiu, et al. Determination of sample entropy and fuzzy measure entropy parameters for distinguishing congestive heart failure from normal sinus rhythm subjects[J]. Entropy, 2015, 17(9): 6270-6288.
17.	ZHANG X S, ROY R J, JENSEN E W. EEG complexity as a measure of depth of anesthesia for patients[J]. Biomedical Engineering, IEEE Transactions on, 2001, 48(12): 1424-1433.
18.	ZHANG X S, ZHU Y S, THAKOR N V, et al. Detecting ventricular tachycardia and fibrillation by complexity measure[J]. IEEE Trans Biomed Eng, 1999, 46(5): 548-555.
19.	JIN D, LIN S. Advances in computer science, intelligent systems and environment: 2[M]. [S.l.]: Springer Science & Business Media, 2011: 493-495.
20.	COSTA M, GOLDBERGER A L, PENG C K. Multiscale entropy analysis of complex physiologic time series[J]. Phys Rev Lett, 2002, 89(6): 068102.
21.	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):120511-120512.
22.	CAO Y H, WTUNG W, GAO J B, et al. Detecting dynamical changes in time series using the permutation entropy[J]. Physical Review E, 2004, 70(4): 1-6.
23.	ZOZOR S, MATEOS D, LAMBERTI P W. Mixing Bandt-Pompe and Lempel-Ziv approaches: another way to analyze the complexity of continuous-state sequences[J]. Eur Phys J B, 2014, 87(5): 1-12.
24.	Physionet. Congestive Heart Failure RR Interval Database [DB/OL].(2016-6-15) [2016-7-28]. http://physionetorg/physiobank/database/chf2db/.
25.	Physionet. Normal Sinus Rhythm RR Interval Database[DB/OL].(2016-6-15) [2016-7-28]. http://physionetorg/physiobank/database/nsr2db/.
26.	ATAMAN E, AATRE V, WONG K. A fast method for real-time median filtering[J]. IEEE Trans Acoustics, Speech and Signal Processing, 1980, 28(4): 415-421.
27.	ZHANG Y T, LIU C Y, WEI S S, et al. ECG quality assessment using a kernel support vector machine and genetic algorithm with a feature matrix[J]. Journal of Zhejiang University-SCIENCE C(Compute & Electron), 2014, 15(7): 564-573.
28.	LIU Chengyu, LIU Changchun, SHAO Peng, et al. Comparison of different threshold values r for approximate entropy: application to investigate the heart rate variability between heart failure and healthy control groups[J]. Physiol Meas, 2011, 32(2): 167-180.

1. 姚文坡, 刘铁兵,戴加飞,等.脑电信号的多尺度排列熵分析[J].物理学报,2014,63(7):078701-078704.
2. LEMPEL A, ZIV J. On the complexity of finite sequences[J]. IEEE Transactions on Information Theory, 1976, 22(1): 75-81.
3. SARLABOUS L, TORRES A, FIZ J A, et al. Multistate Lempel-Ziv (MLZ) Index Interpretation as a Measure of Amplitude and Complexity Changes [M]. 31st Annual International Conference of the IEEE EMBS. Minneapolis, Minnesota, USA. 2009: 4375-4378.
4. SARLABOUS L, TORRES A, FIZ J A, et al. Index for estimation of muscle force from mechanomyography based on the Lempel-Ziv algorithm[J]. Journal of Electromyography and Kinesiology, 2013, 23(3): 548-557.
5. ABOY M, HORNERO R, ABASOLO D, et al..interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis[J]. IEEE Trans Biomed Eng, 2006, 53(11): 2282-2288.
6. RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
7. 黄晓林, 霍铖宇,司峻峰,等.等概率符号化样本熵应用于脑电分析[J].物理学报,2014,63(10):100501-100503.
8. KALPAKIS K, YANG S, HU P F, et al. Permutation entropy analysis of vital signs data for outcome prediction of patients with severe traumatic brain injury[J]. Comput Biol Med, 2015(56): 167-174.
9. ZANIN M, ZUNINO L, ROSSO O A, et al. Permutation entropy and its main biomedical and econophysics applications: a review[J]. Entropy, 2012, 14(8): 1553-1577.
10. ZHANG Y T, WEI S S, LONG Y T, et al. Performance analysis of multiscale entropy for the assessment of ECG signal quality[J]. Journal of Electrical and Computer Engineering, 2015: 1-6.
11. COSTA M, GOLDBERGER A L, PENG C K. Multiscale entropy analysis of biological signals[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2005, 71(2 Pt 1): 021906.
12. ZHANG Xu, CHEN Xiang, BARKHAUS P E, et al. Multiscale entropy analysis of different spontaneous motor unit discharge patterns[J]. IEEE J Biomed Health Inform, 2013, 17(2): 470-476.
13. ABÁSOLO D, ALCARAZ R, RIETA J J, et al. Lempel-Ziv complexity analysis for the evaluation of atrial fibrillation organization; proceedings of the 8th IASTED International Conference on Biomedical Engineering[C].Innsbruck, Austria, 2011: 1-7.
14. ABÁSOLO D, HORNERO R, G M G, et al. Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure[J]. Med Eng Phys, 2006, 28(4): 315-322.
15. RIEDL M, M L M, WESSEL N. Practical considerations of permutation entropy: A tutorial review[J]. Eur Phys J Spec Top, 2013, 222(2): 249-262.
16. ZHAO Lina, WEI Shoushui, ZHANG Chengqiu, et al. Determination of sample entropy and fuzzy measure entropy parameters for distinguishing congestive heart failure from normal sinus rhythm subjects[J]. Entropy, 2015, 17(9): 6270-6288.
17. ZHANG X S, ROY R J, JENSEN E W. EEG complexity as a measure of depth of anesthesia for patients[J]. Biomedical Engineering, IEEE Transactions on, 2001, 48(12): 1424-1433.
18. ZHANG X S, ZHU Y S, THAKOR N V, et al. Detecting ventricular tachycardia and fibrillation by complexity measure[J]. IEEE Trans Biomed Eng, 1999, 46(5): 548-555.
19. JIN D, LIN S. Advances in computer science, intelligent systems and environment: 2[M]. [S.l.]: Springer Science & Business Media, 2011: 493-495.
20. COSTA M, GOLDBERGER A L, PENG C K. Multiscale entropy analysis of complex physiologic time series[J]. Phys Rev Lett, 2002, 89(6): 068102.
21. 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):120511-120512.
22. CAO Y H, WTUNG W, GAO J B, et al. Detecting dynamical changes in time series using the permutation entropy[J]. Physical Review E, 2004, 70(4): 1-6.
23. ZOZOR S, MATEOS D, LAMBERTI P W. Mixing Bandt-Pompe and Lempel-Ziv approaches: another way to analyze the complexity of continuous-state sequences[J]. Eur Phys J B, 2014, 87(5): 1-12.
24. Physionet. Congestive Heart Failure RR Interval Database [DB/OL].(2016-6-15) [2016-7-28]. http://physionetorg/physiobank/database/chf2db/.
25. Physionet. Normal Sinus Rhythm RR Interval Database[DB/OL].(2016-6-15) [2016-7-28]. http://physionetorg/physiobank/database/nsr2db/.
26. ATAMAN E, AATRE V, WONG K. A fast method for real-time median filtering[J]. IEEE Trans Acoustics, Speech and Signal Processing, 1980, 28(4): 415-421.
27. ZHANG Y T, LIU C Y, WEI S S, et al. ECG quality assessment using a kernel support vector machine and genetic algorithm with a feature matrix[J]. Journal of Zhejiang University-SCIENCE C(Compute & Electron), 2014, 15(7): 564-573.
28. LIU Chengyu, LIU Changchun, SHAO Peng, et al. Comparison of different threshold values r for approximate entropy: application to investigate the heart rate variability between heart failure and healthy control groups[J]. Physiol Meas, 2011, 32(2): 167-180.

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