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

Jianjun Yan; Xianglei Cai; Songye Chen; Rui Guo; Haixia Yan; Yiqin Wang

doi:10.2196/28039

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

JMIR Med Inform. 2021 Oct 21;9(10):e28039. doi: 10.2196/28039.

Authors

Jianjun Yan¹, Xianglei Cai¹, Songye Chen¹, Rui Guo², Haixia Yan², Yiqin Wang²

Affiliations

¹ Institute of Intelligent Perception and Diagnosis, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
² Shanghai Key Laboratory of Health Identification and Assessment, Laboratory of Traditional Chinese Medicine for Diagnostic Information, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

PMID: 34673537
PMCID: PMC8569546
DOI: 10.2196/28039

Abstract

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.

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

©Jianjun Yan, Xianglei Cai, Songye Chen, Rui Guo, Haixia Yan, Yiqin Wang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.10.2021.