Yan Jianjun, Cai Xianglei, Chen Songye, Guo Rui, Yan Haixia, Wang Yiqin
Institute of Intelligent Perception and Diagnosis, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
Shanghai Key Laboratory of Health Identification and Assessment, Laboratory of Traditional Chinese Medicine for Diagnostic Information, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
JMIR Med Inform. 2021 Oct 21;9(10):e28039. doi: 10.2196/28039.
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.
The objective of this paper was to comprehensively use machine learning and deep learning classification methods to fully exploit the information about pulse signals.
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.
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.
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.
在脉搏信号分析与识别中，时域和时频域分析方法能够获取可解释的结构化数据，并使用传统机器学习方法构建分类模型。非结构化数据，如脉搏信号，包含有关心血管系统状态的丰富信息，并且可以使用深度学习提取非结构化数据的局部特征并进行分类。
本文的目的是综合运用机器学习和深度学习分类方法，充分挖掘脉搏信号中的信息。
采用时域和时频域分析方法获取结构化数据。使用支持向量机（SVM）构建分类模型，利用深度卷积神经网络（DCNN）内核提取非结构化数据的局部特征，并采用堆叠方法融合上述分类结果进行决策。
仅使用单个分类器时获得的最高平均准确率为0.7914，而使用集成学习方法获得的平均准确率为0.8330。
集成学习可以通过决策级融合有效地利用结构化和非结构化数据中的信息来提高分类准确率。本研究为脉搏信号分类提供了新的思路和方法，对脉搏诊断客观化具有实际价值。
