Measuring complexity using FuzzyEn, ApEn, and SampEn

Medical Engineering and Physics

Volumn 31, Issue 1, 2009, Pages 61-68

  Chen, Weiting ^a   Zhuang, Jun ^b   Yu, Wangxin ^b   Wang, Zhizhong ^b  

^a EAST CHINA NORMAL UNIVERSITY   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

ApEn; Complexity; FuzzyEn; Nonlinear; SampEn

Indexed keywords

ELECTRIC NETWORK ANALYSIS; FUNCTIONS; FUZZY SETS; PROBABILITY DENSITY FUNCTION; TIME SERIES ANALYSIS; VECTORS;

APEN; COMPLEXITY; FUZZYEN; NONLINEAR; SAMPEN;

MEMBERSHIP FUNCTIONS;

ARTICLE; ELECTROMYOGRAM; ENTROPY; FUZZY SYSTEM; NONLINEAR SYSTEM; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; STOICHIOMETRY; THEORETICAL MODEL;

COMPUTER SIMULATION; ELECTROMYOGRAPHY; ENTROPY; FUZZY LOGIC; MODELS, BIOLOGICAL; PROBABILITY; REPRODUCIBILITY OF RESULTS;

EID: 57849119711     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2008.04.005     Document Type: Article

References (25)

1. Yu D., Small M., Harrison R.G., and Diks C. Efficient implementation of the Gaussian kernel algorithm in estimating invariants and noise level from noisy time series data. Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 61 4A (2000) 3750-3756
2. Pincus S.M. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America 88 6 (1991) 2297-2301
3. Bruhn J., Röpcke H., and Hoeft A. Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia. Anesthesiology 92 3 (2000) 715-726
4. Koskinen M., Seppänen T., Tong S., Mustola S., and Thakor N.V. Monotonicity of approximate entropy during transition from awareness to unresponsiveness due to propofol anesthetic induction. IEEE Transactions on Biomedical Engineering 53 4 (2006) 669-675
5. Burioka N., Cornélissen G., Maegaki Y., Halberg F., Kaplan D.T., Miyata M., et al. Approximate entropy of the electroencephalogram in healthy awake subjects and absence epilepsy patients. Clinical EEG and Neuroscience 36 3 (2005) 188-193
6. Pincus S.M., Mulligan T., Iranmanesh A., Gheorghiu S., Godschalk M., and Veldhuis J.D. Older males secrete luteinizing hormone and testosterone more irregularly, and jointly more asynchronously, than younger males. Proceedings of the National Academy of Sciences of the United States of America 93 24 (1996) 14100-14105
7. Christen J.A., Torres J.L., and Barrera J. A statistical feature of genetic sequences. Biometrical Journal 40 7 (1998) 855-863
8. Pincus S.M., Cummins T.R., and Haddad G.G. Heart rate control in normal and aborted-SIDS infants. American Journal of Physiology: Regulatory Integrative and Comparative Physiology 264 3 33-3 (1993) R638-R646
9. Pincus S.M., and Keefe D.L. Quantification of hormone pulsatility via an approximate entropy algorithm. American Journal of Physiology: Endocrinology and Metabolism 262 5 25-5 (1992) E741-E754
10. Caldirola D., Bellodi L., Caumo A., Migliarese G., and Perna G. Approximate entropy of respiratory patterns in panic disorder. American Journal of Psychiatry 161 1 (2004) 79-87
11. Storella R.J., Wood H.W., Mills K.M., Kanters J.K., Høgaard M.V., and Holstein-Rathlou N.H. Approximate entropy and point correlation dimension of heart rate variability in healthy subjects. Integrative Physiological and Behavioral Science 33 4 (1998) 315-320
12. Shin D.G., Yoo C.S., Yi S.H., Bae J.H., Kim Y.J., Park J.S., et al. Prediction of paroxysmal atrial fibrillation using nonlinear analysis of the R-R interval dynamics before the spontaneous onset of atrial fibrillation. Circulation Journal 70 1 (2006) 94-99
13. Ning X., Xu Y., Wang J., and Ma X. Approximate entropy analysis of short-term HFECG based on wave mode. Physica A: Statistical Mechanics and its Applications 346 3-4 (2005) 475-483
14. Radhakrishnan N., Wilson J.D., Lowery C., Murphy P., and Eswaran H. Testing for nonlinearity of the contraction segments in uterine electromyography. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering 10 12 (2000) 2785-2790
15. Pincus S.M. Approximate entropy (ApEn) as a complexity measure. Chaos 5 1 (1995) 110-117
16. Grouven U., Beger F.A., Schultz B., and Schultz A. Correlation of Narcotrend Index, entropy measures, and spectral parameters with calculated propofol effect-site concentrations during induction of propofol-remifentanil anaesthesia. Journal of Clinical Monitoring and Computing 18 4 (2005) 231-240
17. Pincus S.M., and Goldberger A.L. Physiological time-series analysis: what does regularity quantify?. American Journal of Physiology: Heart and Circulatory Physiology 266 4 35-4 (1994) H1643-H1656
18. Richman J.S., and Moorman J.R. Physiological time-series analysis using approximate and sample entropy. American Journal of Physiology: Heart and Circulatory Physiology 278 6 47-6 (2000) H2039-H2049
19. Chen W., Wang Z., Xie H., and Yu W. Characterization of surface EMG signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15 2 (2007) 266-272
20. Zadeh L.A. Fuzzy sets. Information and Control 8 (1965) 338-353
21. Leontitsis A., Bountis T., and Pagge J. An adaptive way for improving noise reduction using local geometric projection. Chaos 14 1 (2004) 106-110
22. Davies D.L., and Bouldin D.W. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1 4 (1979) 224-227
23. Zardoshti-Kermani M., Wheeler B.C., Badie K., and Hashemi R.M. EMG feature evaluation for movement control of upper extremity prostheses. IEEE Transactions on Rehabilitation Engineering 3 4 (1995) 324-333
24. Boostani R., and Moradi M.H. Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiological Measurement 24 2 (2003) 309-319
25. Micera S., Sabatini A.M., Dario P., and Rossi B. A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques. Medical Engineering and Physics 21 5 (1999) 303-311