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
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.
. [J]. Frontiers of Medicine, 2014, 8(3): 279-284.
Yan Ma, Shuchen Sun, Chung-Kang Peng. Applications of dynamical complexity theory in traditional Chinese medicine. Front. Med., 2014, 8(3): 279-284.
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
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
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