Abstract：The pulse diagnosis detects the circulation status of Qi and blood in human body with the help of pulse so as to guide
disease diagnosis and treatment, prediction of the disease and health care. However, the bottleneck problem in the development of
pulse instruments is how to extract pulse information from weak pulse signals. This paper reviewed recent studies about used basic
machine learning (ML) algorithms, neural network algorithms, and ensemble learning algorithms to build a pulse classification
models, starting with issues such as the characteristics of pulse signals, classification of pulse conditions in traditional Chinese
medicine, and the challenges they face. The purpose is to explore the feasibility and effectiveness of establishing models to classify
pulse phases based on ML algorithms by comparing the performances of different ML algorithms and experimental protocols from the
view point of the classification accuracy of different pulse phases, aims to provide a reference for the development of pulse instruments.
田紫微，贾芸芳. 脉象仪中的智能分类算法研究[J]. 中国医疗设备, 2024, 39(3): 146-153.
TIAN Ziwei, JIA Yunfang. Study on Intelligent Classification Algorithms in Pulse Instruments. China Medical Devices, 2024, 39(3): 146-153.
