Applications of dynamical complexity theory in traditional Chinese medicine

Yan Ma, Shuchen Sun, Chung-Kang Peng

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Front. Med. ›› 2014, Vol. 8 ›› Issue (3) : 279-284. DOI: 10.1007/s11684-014-0367-6
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Applications of dynamical complexity theory in traditional Chinese medicine

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Abstract

Traditional Chinese medicine (TCM) has been gradually accepted by the world. Despite its widespread use in clinical settings, a major challenge in TCM is to study it scientifically. This difficulty arises from the fact that TCM views human body as a complex dynamical system, and focuses on the balance of the human body, both internally and with its external environment. As a result, conventional tools that are based on reductionist approach are not adequate. Methods that can quantify the dynamics of complex integrative systems may bring new insights and utilities about the clinical practice and evaluation of efficacy of TCM. The dynamical complexity theory recently proposed and its computational algorithm, Multiscale Entropy (MSE) analysis, are consistent with TCM concepts. This new system level analysis has been successfully applied to many health and disease related topics in medicine. We believe that there could be many promising applications of this dynamical complexity concept in TCM. In this article, we propose some promising applications and research areas that TCM practitioners and researchers can pursue.

Keywords

traditional Chinese medicine / Multiscale Entropy / dynamical complexity / system level / applications

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Yan Ma, Shuchen Sun, Chung-Kang Peng. Applications of dynamical complexity theory in traditional Chinese medicine. Front. Med., 2014, 8(3): 279‒284 https://doi.org/10.1007/s11684-014-0367-6

References

[1]
National Center for Complementary and Alternative Medicine, NIH. Traditional Chinese Medicine: An Introduction. 2010.
[2]
Matuk C. Seeing the body: the divergence of ancient Chinese and western medical illustration. J Biocommun 2006; 32(1): 8
[3]
Xutian S, Zhang J, Louise W. New exploration and understanding of traditional Chinese medicine. Am J Chin Med 2009; 37(3): 411–426
CrossRef Pubmed Google scholar
[4]
Kaptchuk T. The Web That Has No Weaver: understanding Chinese medicine. New York: McGraw-Hill, 2000
[5]
Jordan JB, Tu X. Advances in heroin addiction treatment with traditional Chinese medicine: a systematic review of recent Chinese language journals. Am J Chin Med 2008; 36(3): 437–447
CrossRef Pubmed Google scholar
[6]
Zhang NL, Yuan S, Chen T, Wang Y. Statistical validation of traditional chinese medicine theories. J Altern Complement Med 2008; 14(5): 583–587
CrossRef Pubmed Google scholar
[7]
Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002; 89(6): 068102
CrossRef Pubmed Google scholar
[8]
Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys 2005; 71(2 Pt 1): 021906
CrossRef Pubmed Google scholar
[9]
Peng CK, Costa M, Goldberger AL. Adaptive data analysis of complex fluctuations in physiologic time series. Adv Adapt Data Anal 2009; 1(1): 61–70
CrossRef Pubmed Google scholar
[10]
Nyquist H. Thermal agitation of electric charge in conductors. Phys Rev 1928; 21: 4
[11]
Wayne PM, Manor B, Novak V, Costa MD, Hausdorff JM, Goldberger AL, Ahn AC, Yeh GY, Peng CK, Lough M, Davis RB, Quilty MT, Lipsitz LA. A systems biology approach to studying Tai Chi, physiological complexity and healthy aging: design and rationale of a pragmatic randomized controlled trial. Contemp Clin Trials 2013; 34(1): 21–34
CrossRef Pubmed Google scholar
[12]
Bari V, Valencia JF, Vallverdu M, Girardengo G, Bassani T, Marchi A, Calvillo L, Caminal P, Cerutti S, Brink PA, Crotti L, Schwartz PJ, Porta A. Refined multiscale entropy analysis of heart period and QT interval variabilities in long QT syndrome type-1 patients. Conf Proc IEEE Eng Med Biol Soc 2013; 2013: 5554–5557
Pubmed
[13]
Wu HT, Hsu PC, Lin CF, Wang HJ, Sun CK, Liu AB, Lo MT, Tang CJ. Multiscale entropy analysis of pulse wave velocity for assessing atherosclerosis in the aged and diabetic. IEEE Trans Biomed Eng 2011; 58(10): 2978–2981
CrossRef Pubmed Google scholar
[14]
Ho YL, Lin C, Lin YH, Lo MT. The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure—a pilot study of multiscale entropy. PLoS ONE 2011; 6(4): e18699
CrossRef Pubmed Google scholar
[15]
Baumert M, Javorka M, Seeck A, Faber R, Sanders P, Voss A. Multiscale entropy and detrended fluctuation analysis of QT interval and heart rate variability during normal pregnancy. Comput Biol Med 2012; 42(3): 347–352
CrossRef Pubmed Google scholar
[16]
Turianikova Z, Javorka K, Baumert M, Calkovska A, Javorka M. The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure. Physiol Meas 2011; 32(9): 1425–1437
CrossRef Pubmed Google scholar
[17]
Trunkvalterova Z, Javorka M, Tonhajzerova I, Javorkova J, Lazarova Z, Javorka K, Baumert M. Reduced short-term complexity of heart rate and blood pressure dynamics in patients with diabetes mellitus type 1: multiscale entropy analysis. Physiol Meas 2008; 29(7): 817–828
CrossRef Pubmed Google scholar
[18]
Papaioannou VE, Chouvarda I, Maglaveras N, Dragoumanis C, Pneumatikos I. Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: a study of physiologic complexity in surgical critically ill patients. J Crit Care 2011; 26(3): 262–272
CrossRef Pubmed Google scholar
[19]
Bell IR, Howerter A, Jackson N, Aickin M, Bootzin RR, Brooks AJ. Nonlinear dynamical systems effects of homeopathic remedies on multiscale entropy and correlation dimension of slow wave sleep EEG in young adults with histories of coffee-induced insomnia. Homeopathy 2012; 101(3): 182–192
CrossRef Pubmed Google scholar
[20]
Catarino A, Churches O, Baron-Cohen S, Andrade A, Ring H. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clin Neurophysiol 2011; 122(12): 2375–2383
CrossRef Pubmed Google scholar
[21]
Mizuno T, Takahashi T, Cho RY, Kikuchi M, Murata T, Takahashi K, Wada Y. Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clin Neurophysiol 2010; 121(9): 1438–1446
CrossRef Pubmed Google scholar
[22]
Takahashi T, Cho RY, Mizuno T, Kikuchi M, Murata T, Takahashi K, Wada Y. Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 2010; 51(1): 173–182
CrossRef Pubmed Google scholar
[23]
Takahashi T, Cho RY, Murata T, Mizuno T, Kikuchi M, Mizukami K, Kosaka H, Takahashi K, Wada Y. Age-related variation in EEG complexity to photic stimulation: a multiscale entropy analysis. Clin Neurophysiol 2009; 120(3): 476–483
CrossRef Pubmed Google scholar
[24]
Heisz J J, McIntosh AR. Applications of EEG neuroimaging data: event-related potentials, spectral power, and multiscale entropy. J Vis Exp 2013; (76): e50131
CrossRef Google scholar
[25]
Papaioannou VE, Chouvarda IG, Maglaveras NK, Baltopoulos GI, Pneumatikos IA. Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients. Physiol Meas 2013; 34(11): 1449–1466
CrossRef Pubmed Google scholar
[26]
Papaioannou VE, Chouvarda IG, Maglaveras NK, Pneumatikos IA. Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock. Crit Care 2012; 16(2): R51
CrossRef Pubmed Google scholar
[27]
Istenič, R, Kaplanis PA, Pattichis CS, Zazula D. Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders. Med Biol Eng Comput 2010; 48(8): 773–781
CrossRef Google scholar
[28]
Zhang X, Chen X, Barkhaus PE, Zhou P. Multiscale entropy analysis of different spontaneous motor unit discharge patterns. IEEE J Biomed Health Inform 2013; 17(2): 470–476
CrossRef Pubmed Google scholar
[29]
Huang CW, Sue PD, Abbod MF, Jiang BC, Shieh JS. Measuring center of pressure signals to quantify human balance using multivariate multiscale entropy by designing a force platform. Sensors (Basel) 2013; 13(8): 10151–10166
CrossRef Pubmed Google scholar
[30]
Fernandez JW, Shim VB, Hunter PJ. Integrating degenerative mechanisms in bone and cartilage: a multiscale approach. Conf Proc IEEE Eng Med Biol Soc 2012; 2012: 6616–6619
Pubmed
[31]
Khandoker AH, Karmakar CK, Begg RK, Palaniswami M. Wavelet-based multiscale analysis of minimum toe clearance variability in the young and elderly during walking. Conf Proc IEEE Eng Med Biol Soc 2007; 2007: 1558–1561
CrossRef Pubmed Google scholar
[32]
Gruber AH, Busa MA, Gorton III GE, Van Emmerik RE, Masso PD, Hamill J. Time-to-contact and multiscale entropy identify differences in postural control in adolescent idiopathic scoliosis. Gait Posture 2011; 34(1): 13–18
CrossRef Pubmed Google scholar
[33]
Yang AC, Tsai SJ. Is mental illness complex? From behavior to brain. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 253–257
CrossRef Pubmed Google scholar
[34]
Yang AC, Tsai SJ. Complexity of mental illness: a new research dimension. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 251–252
CrossRef Pubmed Google scholar
[35]
Takahashi T. Complexity of spontaneous brain activity in mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 258–266
CrossRef Pubmed Google scholar
[36]
Wu HT, Lo MT, Chen GH, Sun CK, Chen JJ. Novel application of a multiscale entropy index as a sensitive tool for detecting subtle vascular abnormalities in the aged and diabetic. Comput Math Methods Med 2013; 2013: 645702
CrossRef Pubmed Google scholar
[37]
Viceconti M, Taddei F, Van Sint Jan S, Leardini A, Cristofolini L, Stea S, Baruffaldi F, Baleani M. Multiscale modelling of the skeleton for the prediction of the risk of fracture. Clin Biomech (Bristol, Avon) 2008; 23(7): 845–852
CrossRef Pubmed Google scholar
[38]
Liang WK, Lo MT, Yang AC, Peng CK, Cheng SK, Tseng P, Juan CH. Revealing the brain’s adaptability and the transcranial direct current stimulation facilitating effect in inhibitory control by multiscale entropy. Neuroimage 2014; 90: 218–234
CrossRef Pubmed Google scholar
[39]
Kang HG, Costa MD, Priplata AA, Starobinets OV, Goldberger AL, Peng CK, Kiely DK, Cupples LA, Lipsitz LA. Frailty and the degradation of complex balance dynamics during a dual-task protocol. J Gerontol A Biol Sci Med Sci 2009; 64(12): 1304–1311
CrossRef Pubmed Google scholar
[40]
Lu A, Jiang M, Zhang C, Chan K. An integrative approach of linking traditional Chinese medicine pattern classification and biomedicine diagnosis. J Ethnopharmacol 2012; 141(2): 549–556
CrossRef Pubmed Google scholar
[41]
Jiang M, Lu C, Zhang C, Yang J, Tan Y, Lu A, Chan K. Syndrome differentiation in modern research of traditional Chinese medicine. J Ethnopharmacol 2012; 140(3): 634–642
CrossRef Pubmed Google scholar
[42]
Wang JH. Traditional Chinese medicine and the positive correlation with homeostatic evolution of human being: based on medical perspective. Chin J Integr Med 2012; 18(8): 629–634
CrossRef Pubmed Google scholar
[43]
Fan XJ, Yu H, Ren J. Homeostasis and compensatory homeostasis: bridging western medicine and traditional Chinese medicine. Curr Cardiol Rev 2011; 7(1): 43–46
CrossRef Pubmed Google scholar
[44]
Roberti di Sarsina P, Alivia M, Guadagni P. Traditional, complementary and alternative medical systems and their contribution to personalisation, prediction and prevention in medicine-person-centred medicine. EPMA J 2012; 3(1): 15
CrossRef Pubmed Google scholar
[45]
Yang HJ, Shen D, Xu HY, Lu P. A new strategy in drug design of Chinese medicine: theory, method and techniques. Chin J Integr Med 2012; 18(11): 803–806
CrossRef Pubmed Google scholar
[46]
Wang M, Lamers RJ, Korthout HA, van Nesselrooij JH, Witkamp RF, van der Heijden R, Voshol PJ, Havekes LM, Verpoorte R, van der Greef J. Metabolomics in the context of systems biology: bridging traditional Chinese medicine and molecular pharmacology. Phytother Res 2005; 19(3): 173–182
CrossRef Pubmed Google scholar

Compliance with ethics guidelines

Dr. Yan Ma, Prof. Shuchen Sun, and Prof. Chung-Kang Peng declare that they have no conflict of interest. This manuscript is a review article and does not involve a research protocol requiring approval by the relevant institutional review board or ethics committee.

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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