Role of Parameter Setting in Electroacupuncture: Current Scenario and Future Prospects.
10.1007/s11655-020-3269-2
- Author:
Yuan-Yuan ZHANG
1
;
Qi-Liang CHEN
2
;
Qiong WANG
3
;
Shan-Shan DING
2
;
Shu-Nan LI
2
;
Shu-Jiao CHEN
2
;
Xue-Juan LIN
2
;
Can-Dong LI
4
;
Tetsuya ASAKAWA
5
Author Information
1. College of Acupuncture and Moxibustion, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China.
2. Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China.
3. Hangzhou Changgentang Clinic of Traditional Chinese Medicine, Hangzhou, 310009, China.
4. Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China. fjzylcd@126.com.
5. Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China. asakawat1971@gmail.com.
- Publication Type:Review
- Keywords:
Chinese medicine;
acupuncture;
artificial intelligence;
electroacupuncture;
parameter;
stimulation
- MeSH:
Acupuncture Points;
Acupuncture Therapy;
Artificial Intelligence;
Electroacupuncture;
Humans
- From:
Chinese journal of integrative medicine
2022;28(10):953-960
- CountryChina
- Language:English
-
Abstract:
Acupuncture is an ancient therapeutic method based on the theory of Chinese medicine (CM). Traditional acupuncture has many limitations; it is subjective and relies more on the experience of an acupuncturist, and the efficacy is sometimes irreproducible. In contrast, electroacupuncture (EA) has special characteristics in terms of objectivity and stability, thereby gaining considerable attention. Parameter setting plays a crucial role in EA practice. The current paper summarizes the current situation and limitations of parameter setting in EA practice. Objectification is the tendency and future of CM as well as EA. With the development of computerized technologies, such as wearable sensors, vast data, and artificial intelligence, CM syndromes can be successfully objectified. We propose the development of a novel self-feedback-adjust EA system, which may improve the parameter setting in EA and be beneficial to both the patients and clinicians.
