Please wait a minute...
Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2014, Vol. 8 Issue (3) : 279-284    https://doi.org/10.1007/s11684-014-0367-6
REVIEW
Applications of dynamical complexity theory in traditional Chinese medicine
Yan Ma1,2, Shuchen Sun3, Chung-Kang Peng1,4()
1. Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
2. Sleep Center, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing 100040, China
3. Department of Otolaryngology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
4. Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li, Taiwan, China
 Download: PDF(109 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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     
Corresponding Author(s): Chung-Kang Peng   
Online First Date: 09 September 2014    Issue Date: 09 October 2014
 Cite this article:   
Yan Ma,Shuchen Sun,Chung-Kang Peng. Applications of dynamical complexity theory in traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 279-284.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-014-0367-6
https://academic.hep.com.cn/fmd/EN/Y2014/V8/I3/279
1 National Center for Complementary and Alternative Medicine, NIH. Traditional Chinese Medicine: An Introduction. 2010.
2 C Matuk. Seeing the body: the divergence of ancient Chinese and western medical illustration. J Biocommun 2006; 32(1): 8
3 S Xutian, J Zhang, W Louise. New exploration and understanding of traditional Chinese medicine. Am J Chin Med 2009; 37(3): 411–426
https://doi.org/10.1142/S0192415X09006941 pmid: 19606504
4 T Kaptchuk. The Web That Has No Weaver: understanding Chinese medicine. New York: McGraw-Hill, 2000
5 JB Jordan, X Tu. 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
https://doi.org/10.1142/S0192415X08005886 pmid: 18543379
6 NL Zhang, S Yuan, T Chen, Y Wang. Statistical validation of traditional chinese medicine theories. J Altern Complement Med 2008; 14(5): 583–587
https://doi.org/10.1089/acm.2007.7019 pmid: 18554082
7 M Costa, AL Goldberger, CK Peng. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002; 89(6): 068102
https://doi.org/10.1103/PhysRevLett.89.068102 pmid: 12190613
8 M Costa, AL Goldberger, CK Peng. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys 2005; 71(2 Pt 1): 021906
https://doi.org/10.1103/PhysRevE.71.021906 pmid: 15783351
9 CK Peng, M Costa, AL Goldberger. Adaptive data analysis of complex fluctuations in physiologic time series. Adv Adapt Data Anal 2009; 1(1): 61–70
https://doi.org/10.1142/S1793536909000035 pmid: 20041035
10 H Nyquist. Thermal agitation of electric charge in conductors. Phys Rev 1928; 21: 4
11 PM Wayne, B Manor, V Novak, MD Costa, JM Hausdorff, AL Goldberger, AC Ahn, GY Yeh, CK Peng, M Lough, RB Davis, MT Quilty, LA Lipsitz. 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
https://doi.org/10.1016/j.cct.2012.09.006 pmid: 23026349
12 V Bari, JF Valencia, M Vallverdu, G Girardengo, T Bassani, A Marchi, L Calvillo, P Caminal, S Cerutti, PA Brink, L Crotti, PJ Schwartz, A Porta. 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
pmid: 24110995
13 HT Wu, PC Hsu, CF Lin, HJ Wang, CK Sun, AB Liu, MT Lo, CJ Tang. Multiscale entropy analysis of pulse wave velocity for assessing atherosclerosis in the aged and diabetic. IEEE Trans Biomed Eng 2011; 58(10): 2978–2981
https://doi.org/10.1109/TBME.2011.2159975 pmid: 21693413
14 YL Ho, C Lin, YH Lin, MT Lo. 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
https://doi.org/10.1371/journal.pone.0018699 pmid: 21533258
15 M Baumert, M Javorka, A Seeck, R Faber, P Sanders, A Voss. Multiscale entropy and detrended fluctuation analysis of QT interval and heart rate variability during normal pregnancy. Comput Biol Med 2012; 42(3): 347–352
https://doi.org/10.1016/j.compbiomed.2011.03.019 pmid: 21530956
16 Z Turianikova, K Javorka, M Baumert, A Calkovska, M Javorka. The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure. Physiol Meas 2011; 32(9): 1425–1437
https://doi.org/10.1088/0967-3334/32/9/006 pmid: 21799239
17 Z Trunkvalterova, M Javorka, I Tonhajzerova, J Javorkova, Z Lazarova, K Javorka, M Baumert. 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
https://doi.org/10.1088/0967-3334/29/7/010 pmid: 18583725
18 VE Papaioannou, I Chouvarda, N Maglaveras, C Dragoumanis, I Pneumatikos. 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
https://doi.org/10.1016/j.jcrc.2010.07.010 pmid: 20869842
19 IR Bell, A Howerter, N Jackson, M Aickin, RR Bootzin, AJ Brooks. 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
https://doi.org/10.1016/j.homp.2012.05.007 pmid: 22818237
20 A Catarino, O Churches, S Baron-Cohen, A Andrade, H Ring. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clin Neurophysiol 2011; 122(12): 2375–2383
https://doi.org/10.1016/j.clinph.2011.05.004 pmid: 21641861
21 T Mizuno, T Takahashi, RY Cho, M Kikuchi, T Murata, K Takahashi, Y Wada. Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clin Neurophysiol 2010; 121(9): 1438–1446
https://doi.org/10.1016/j.clinph.2010.03.025 pmid: 20400371
22 T Takahashi, RY Cho, T Mizuno, M Kikuchi, T Murata, K Takahashi, Y Wada. Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 2010; 51(1): 173–182
https://doi.org/10.1016/j.neuroimage.2010.02.009 pmid: 20149880
23 T Takahashi, RY Cho, T Murata, T Mizuno, M Kikuchi, K Mizukami, H Kosaka, K Takahashi, Y Wada. Age-related variation in EEG complexity to photic stimulation: a multiscale entropy analysis. Clin Neurophysiol 2009; 120(3): 476–483
https://doi.org/10.1016/j.clinph.2008.12.043 pmid: 19231279
24 J J Heisz, AR McIntosh. Applications of EEG neuroimaging data: event-related potentials, spectral power, and multiscale entropy. J Vis Exp 2013; (76): e50131
https://doi.org/10.3791/50131
25 VE Papaioannou, IG Chouvarda, NK Maglaveras, GI Baltopoulos, IA Pneumatikos. Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients. Physiol Meas 2013; 34(11): 1449–1466
https://doi.org/10.1088/0967-3334/34/11/1449 pmid: 24149496
26 VE Papaioannou, IG Chouvarda, NK Maglaveras, IA Pneumatikos. 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
https://doi.org/10.1186/cc11255 pmid: 22424316
27 R Istenič,, PA Kaplanis, CS Pattichis, D Zazula. Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders. Med Biol Eng Comput 2010; 48(8): 773–781
https://doi.org/20490940
28 X Zhang, X Chen, PE Barkhaus, P Zhou. Multiscale entropy analysis of different spontaneous motor unit discharge patterns. IEEE J Biomed Health Inform 2013; 17(2): 470–476
https://doi.org/10.1109/JBHI.2013.2241071 pmid: 24235117
29 CW Huang, PD Sue, MF Abbod, BC Jiang, JS Shieh. 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
https://doi.org/10.3390/s130810151 pmid: 23966184
30 JW Fernandez, VB Shim, PJ Hunter. Integrating degenerative mechanisms in bone and cartilage: a multiscale approach. Conf Proc IEEE Eng Med Biol Soc 2012; 2012: 6616–6619
pmid: 23367446
31 AH Khandoker, CK Karmakar, RK Begg, M Palaniswami. 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
https://doi.org/10.1109/IEMBS.2007.4352601 pmid: 18002267
32 AH Gruber, MA Busa, GE Gorton III, RE Van Emmerik, PD Masso, J Hamill. Time-to-contact and multiscale entropy identify differences in postural control in adolescent idiopathic scoliosis. Gait Posture 2011; 34(1): 13–18
https://doi.org/10.1016/j.gaitpost.2011.02.015 pmid: 21478018
33 AC Yang, SJ Tsai. Is mental illness complex? From behavior to brain. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 253–257
https://doi.org/10.1016/j.pnpbp.2012.09.015 pmid: 23089053
34 AC Yang, SJ Tsai. Complexity of mental illness: a new research dimension. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 251–252
https://doi.org/10.1016/j.pnpbp.2013.01.018 pmid: 23380171
35 T Takahashi. Complexity of spontaneous brain activity in mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45: 258–266
https://doi.org/10.1016/j.pnpbp.2012.05.001 pmid: 22579532
36 HT Wu, MT Lo, GH Chen, CK Sun, JJ Chen. 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
https://doi.org/10.1155/2013/645702 pmid: 23509600
37 M Viceconti, F Taddei, S Van Sint Jan, A Leardini, L Cristofolini, S Stea, F Baruffaldi, M Baleani. Multiscale modelling of the skeleton for the prediction of the risk of fracture. Clin Biomech (Bristol, Avon) 2008; 23(7): 845–852
https://doi.org/10.1016/j.clinbiomech.2008.01.009 pmid: 18304710
38 WK Liang, MT Lo, AC Yang, CK Peng, SK Cheng, P Tseng, CH Juan. Revealing the brain’s adaptability and the transcranial direct current stimulation facilitating effect in inhibitory control by multiscale entropy. Neuroimage 2014; 90: 218–234
https://doi.org/10.1016/j.neuroimage.2013.12.048 pmid: 24389016
39 HG Kang, MD Costa, AA Priplata, OV Starobinets, AL Goldberger, CK Peng, DK Kiely, LA Cupples, LA Lipsitz. 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
https://doi.org/10.1093/gerona/glp113 pmid: 19679739
40 A Lu, M Jiang, C Zhang, K Chan. An integrative approach of linking traditional Chinese medicine pattern classification and biomedicine diagnosis. J Ethnopharmacol 2012; 141(2): 549–556
https://doi.org/10.1016/j.jep.2011.08.045 pmid: 21896324
41 M Jiang, C Lu, C Zhang, J Yang, Y Tan, A Lu, K Chan. Syndrome differentiation in modern research of traditional Chinese medicine. J Ethnopharmacol 2012; 140(3): 634–642
https://doi.org/10.1016/j.jep.2012.01.033 pmid: 22322251
42 JH Wang. 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
https://doi.org/10.1007/s11655-012-1170-3 pmid: 22855040
43 XJ Fan, H Yu, J Ren. Homeostasis and compensatory homeostasis: bridging western medicine and traditional Chinese medicine. Curr Cardiol Rev 2011; 7(1): 43–46
https://doi.org/10.2174/157340311795677671 pmid: 22294974
44 P Roberti di Sarsina, M Alivia, P Guadagni. 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
https://doi.org/10.1186/1878-5085-3-15 pmid: 23126628
45 HJ Yang, D Shen, HY Xu, P Lu. A new strategy in drug design of Chinese medicine: theory, method and techniques. Chin J Integr Med 2012; 18(11): 803–806
https://doi.org/10.1007/s11655-012-1270-x pmid: 23086484
46 M Wang, RJ Lamers, HA Korthout, JH van Nesselrooij, RF Witkamp, R van der Heijden, PJ Voshol, LM Havekes, R Verpoorte, J van der Greef. Metabolomics in the context of systems biology: bridging traditional Chinese medicine and molecular pharmacology. Phytother Res 2005; 19(3): 173–182
https://doi.org/10.1002/ptr.1624 pmid: 15934013
[1] Qiaoli Shi, Fei Xia, Qixin Wang, Fulong Liao, Qiuyan Guo, Chengchao Xu, Jigang Wang. Discovery and repurposing of artemisinin[J]. Front. Med., 2022, 16(1): 1-9.
[2] Ling Dai, Xiang Gao, Zhihua Ye, Hanmin Li, Xin Yao, Dingbo Lu, Na Wu. The “Traditional Chinese medicine regulating liver regeneration” treatment plan for reducing mortality of patients with hepatitis B-related liver failure based on real-world clinical data[J]. Front. Med., 2021, 15(3): 495-505.
[3] Danyang Song, Jianyu Hao, Daiming Fan. Biological properties and clinical applications of berberine[J]. Front. Med., 2020, 14(5): 564-582.
[4] Mengxue Huang, Jingjing Wang, Runshun Zhang, Zhuying Ni, Xiaoying Liu, Wenwen Liu, Weilian Kong, Yao Chen, Tiantian Huang, Guihua Li, Dan Wei, Jianzhong Liu, Xuezhong Zhou. Symptom network topological features predict the effectiveness of herbal treatment for pediatric cough[J]. Front. Med., 2020, 14(3): 357-367.
[5] Qiqi Zhao, Xin Gao, Guangli Yan, Aihua Zhang, Hui Sun, Ying Han, Wenxiu Li, Liang Liu, Xijun Wang. Chinmedomics facilitated quality-marker discovery of Sijunzi decoction to treat spleen qi deficiency syndrome[J]. Front. Med., 2020, 14(3): 335-356.
[6] Yuan Gao, Zhilei Wang, Jinfa Tang, Xiaoyi Liu, Wei Shi, Nan Qin, Xiaoyan Wang, Yu Pang, Ruisheng Li, Yaming Zhang, Jiabo Wang, Ming Niu, Zhaofang Bai, Xiaohe Xiao. New incompatible pair of TCM: Epimedii Folium combined with Psoraleae Fructus induces idiosyncratic hepatotoxicity under immunological stress conditions[J]. Front. Med., 2020, 14(1): 68-80.
[7] Hudan Pan, Yanfang Zheng, Zhongqiu Liu, Zhongwen Yuan, Rutong Ren, Hua Zhou, Ying Xie, Liang Liu. Deciphering the pharmacological mechanism of Guan-Jie-Kang in treating rat adjuvant-induced arthritis using omics analysis[J]. Front. Med., 2019, 13(5): 564-574.
[8] Li Ma, Bin Wang, Yuanxiong Long, Hanmin Li. Effect of traditional Chinese medicine combined with Western therapy on primary hepatic carcinoma: a systematic review with meta-analysis[J]. Front. Med., 2017, 11(2): 191-202.
[9] Yunfang Liu,Zhiping Yang,Jing Cheng,Daiming Fan. Barriers and countermeasures in developing traditional Chinese medicine in Europe[J]. Front. Med., 2016, 10(3): 360-376.
[10] Yan Ma,Kehua Zhou,Jing Fan,Shuchen Sun. Traditional Chinese medicine: potential approaches from modern dynamical complexity theories[J]. Front. Med., 2016, 10(1): 28-32.
[11] Xuezhong Zhou,Yubing Li,Yonghong Peng,Jingqing Hu,Runshun Zhang,Liyun He,Yinghui Wang,Lijie Jiang,Shiyan Yan,Peng Li,Qi Xie,Baoyan Liu. Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 337-346.
[12] Runshun Zhang,Yinghui Wang,Baoyan Liu,Guangli Song,Xuezhong Zhou,Shizhen Fan,Xishui Pan. Clinical data quality problems and countermeasure for real world study[J]. Front. Med., 2014, 8(3): 352-357.
[13] Li Ma,Baoyan Liu,Qi Xie,Shusong Mao,Zhiwei Cui. Ontological reconstruction of the clinical terminology of traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 358-361.
[14] Yixin Zhong,Baoyan Liu,Hua Qu,Qi Xie. Methodological challenges to human medical study[J]. Front. Med., 2014, 8(3): 328-336.
[15] Guanli Song,Yinghui Wang,Runshun Zhang,Baoyan Liu,Xuezhong Zhou,Xiaji Zhou,Hong Zhang,Yufeng Guo,Yanxing Xue,Lili Xu. Experience inheritance from famous specialists based on real-world clinical research paradigm of traditional Chinese medicine[J]. Front. Med., 2014, 8(3): 300-309.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed