JMIR Publications

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, while unstructured data like 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 of pulse signals.

Methods:

The structured data is obtained by using time domain and time frequency domain analysis methods, a classification model is built using SVM, the DCNN network convolution kernel is used to extract local features of the unstructured data, a classification model is built, and the Stacking method is used to fuse the above classification results for decision making.

Results:

The highest accuracy of 0.7914 was obtained using only a single classifier, while the accuracy obtained using the ensemble learning approach was 0.8330.

Conclusions:

Ensemble learning can effectively utilize 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.