Exploring Acupoints and Parameter Application Patterns of TEAS for Treating Post-Stroke Limb Dysfunction Based on Data Mining Techniques
Objective:To explore acupoints and parameter application patterns of transcutaneous electrical acupoint stimulation(TEAS)for treating post-stroke limb dysfunction based on data mining techniques.Methods:Clinical research literature on TEAS for post-stroke limb dysfunction were retrieved from CNKI,Wanfang,VIP,SinoMed,PubMed and Embase,starting from the establishment of the database to November 30,2023.Excel 2019,SPSS Modelerl8.0,Cytoscape and SPSS26.0 were used to create a clinical prescription database and perform descriptive analysis,association rule analysis and cluster analysis based on the data.Results:A total of 101 articles were included in the literature,involving 106 prescriptions and 62 acupoints,with application frequency of 466 times in total.Waiguan(TE5)was the most frequently used acupoint,with the most commonly selected meridian being the Hand-Yangming Large Intestine Meridian,and the highest frequency of use was on the upper limbs.He-sea points were often chosen within specific points.The acupoint combination with the highest association is TE5-Shousanli(LI10).Cluster analysis grouped the high-frequency acupoints into four effective categories.The most commonly used stimulation waveform for TEAS was the intermittent wave,with a stimulation frequency of mostly 100 Hz,a duration of 30 mins,and current intensity set at tolerance or maximum tolerance.Conclusion:The acupoint selection pattern of TEAS for treating post-stroke limb dysfunction reflects traditional Chinese medicine principles such as'treating atrophy by selecting Yangming alone','the therapeutic scope of acupoints'and'the balance of Yin and Yang',while also emphasizing the application of specific acupoints.Future research should further deepen the study of TEAS parameters,standardize parameter reporting,and enhance the reproducibility of research.
Transcutaneous electrical stimulation of acupointsStrokeLimb dysfunctionData miningAcupointParameter
国家科技期刊平台
NSTL
万方数据
