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Proceedings Paper

Simulated annealing model of acupuncture
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Paper Abstract

The growth control singularity model suggests that acupuncture points (acupoints) originate from organizers in embryogenesis. Organizers are singular points in growth control. Acupuncture can cause perturbation of a system with effects similar to simulated annealing. In clinical trial, the goal of a treatment is to relieve certain disorder which corresponds to reaching certain local optimum in simulated annealing. The self-organizing effect of the system is limited and related to the person’s general health and age. Perturbation at acupoints can lead a stronger local excitation (analogous to higher annealing temperature) compared to perturbation at non-singular points (placebo control points). Such difference diminishes as the number of perturbed points increases due to the wider distribution of the limited self-organizing activity. This model explains the following facts from systematic reviews of acupuncture trials: 1. Properly chosen single acupoint treatment for certain disorder can lead to highly repeatable efficacy above placebo 2. When multiple acupoints are used, the result can be highly repeatable if the patients are relatively healthy and young but are usually mixed if the patients are old, frail and have multiple disorders at the same time as the number of local optima or comorbidities increases. 3. As number of acupoints used increases, the efficacy difference between sham and real acupuncture often diminishes. It predicted that the efficacy of acupuncture is negatively correlated to the disease chronicity, severity and patient’s age. This is the first biological – physical model of acupuncture which can predict and guide clinical acupuncture research.

Paper Details

Date Published: 20 May 2015
PDF: 6 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960U (20 May 2015); doi: 10.1117/12.2185052
Show Author Affiliations
Charles Shang, Baylor College of Medicine (United States)
Harold Szu, The Catholic Univ. of America (United States)


Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)

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