TY  - JOUR
AU  - Yan, Jianjun
AU  - Cai, Xianglei
AU  - Chen, Songye
AU  - Guo, Rui
AU  - Yan, Haixia
AU  - Wang, Yiqin
PY  - 2021
DA  - 2021/10/21
TI  - Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study
JO  - JMIR Med Inform
SP  - e28039
VL  - 9
IS  - 10
KW  - wrist pulse
KW  - ensemble learning
KW  - support vector machine
KW  - deep convolutional neural network
KW  - pulse signal
KW  - machine learning
KW  - traditional Chinese medicine
KW  - pulse classification
KW  - pulse analysis
KW  - fully connected neural network
KW  - synthetic minority oversampling technique
KW  - feature extraction
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2021/10/e28039
UR  - https://doi.org/10.2196/28039
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34673537
DO  - 10.2196/28039
ID  - info:doi/10.2196/28039
ER  -