A Classification Method of Acupoints and Non-acupoints based on Traditional Features and Wavelet Features

Abstract

Abstract Meridians and acupoints are the basis of TCM theory and play an important role in disease diagnosis and acupuncture treatment. There are still many problems in current research on electrical signals of acupoints. On the one hand, most of the studies did not consider the integrity of the meridian, but only based on a few acupoints. On the other hand, the lack of targeted feature extraction and classification methods leads to unsatisfactory classification results. Considering the above problems, a method combining traditional features and wavelet features is proposed to classify acupoints and non-acupoints. Based on the integrity of the meridians, we first collect the body surface electrical signals of some acupoints and non-acupoints on the twelve meridians of the human body, and then extract traditional and wavelet features from the measured signals. Finally, SVM and XGBoost are used to classify acupoints and non-acupoints respectively. The experimental results show that this method can effectively improve the classification performance of acupoints and non-acupoints, and for the feature vectors constructed in this paper, XGBoost has better classification capabilities.