Key result
An ensemble learning approach combining deep convolutional neural networks and support vector machines achieved an average pulse classification accuracy of 0.8330, compared to 0.7914 for the best single classifier.
Why the study?
Pulse signals contain rich cardiovascular state information across structured and unstructured data, prompting the use of combined machine learning and deep learning methods to fully exploit these signals.
Absolute Event Rate: 83.3% vs 79.1%
An ensemble learning approach combining SVM and DCNN improves the classification accuracy of traditional Chinese medicine pulse signals compared to single classifiers.
May improve pulse classification accuracy in research; extends single classifiers but leaves clinical utility open.
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.
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Yan et al. (2021) studied Pulse signal classification (n=1,812). Ensemble learning approach (DSSN) vs. Single classifiers (SVM, DCNN) was evaluated on Average classification accuracy. An ensemble learning approach combining deep convolutional neural networks and support vector machines achieved an average pulse classification accuracy of 0.8330, compared to 0.7914 for the best single classifier.
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