文献类型：期刊文献

英文题名：Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study

作者：Yan, Jianjun[1];Cai, Xianglei[1];Chen, Songye[1];Guo, Rui[2];Yan, Haixia[2];Wang, Yiqin[2]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Inst Intelligent Percept & Diag, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Shanghai Univ Tradit Chinese Med, Shanghai Key Lab Hlth Identificat & Assessment, Lab Tradit Chinese Med Diagnost Informat, Shanghai, Peoples R China

年份：2021

卷号：9

期号：10

外文期刊名：JMIR MEDICAL INFORMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000712877700008)】；

基金：This study was supported by the National Natural Science Foundation of China (nos. 82074332, 81673880, and 81302913) , the Shanghai Science and Technology Committee Funding (no. 19441901100) , and Shanghai Science and Technology Funding (no. 21DZ2271000) .

语种：英文

外文关键词：wrist pulse; ensemble learning; support vector machine; deep convolutional neural network; pulse signal; machine learning; traditional Chinese medicine; pulse classification; pulse analysis; fully connected neural network; synthetic minority oversampling technique; feature extraction

摘要：Background: In pulse signal analysis and identification, time domain and time frequency domain analysis methods can obtain interpretable structured data and build classification models using traditional machine learning methods. Unstructured data, such as pulse signals, contain rich information about the state of the cardiovascular system, and local features of unstructured data can be extracted and classified using deep learning. Objective: The objective of this paper was to comprehensively use machine learning and deep learning classification methods to fully exploit the information about pulse signals. Methods: Structured data were obtained by using time domain and time frequency domain analysis methods. A classification model was built using a support vector machine (SVM), a deep convolutional neural network (DCNN) kernel was used to extract local features of the unstructured data, and the stacking method was used to fuse the above classification results for decision making. Results: The highest average accuracy of 0.7914 was obtained using only a single classifier, while the average accuracy obtained using the ensemble learning approach was 0.8330. Conclusions: Ensemble learning can effectively use information from structured and unstructured data to improve classification accuracy through decision-level fusion. This study provides a new idea and method for pulse signal classification, which is of practical value for pulse diagnosis objectification.