Acupoint classification method based on improved support vector machine

Juan Tu; Zunxiang Dai; Jianguo Chen

doi:10.1117/12.3009557

16 October 2023 Acupoint classification method based on improved support vector machine

Juan Tu, Zunxiang Dai, Jianguo Chen

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030T (2023) https://doi.org/10.1117/12.3009557
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Acupoint potential signal is a kind of bioelectric signal collected on the skin surface, which has the characteristics of weak signal, strong noise and strong randomness. These characteristics make it difficult to extract acupoint features, which further affects the accuracy of acupoint classification. This paper proposes an acupoint classification method combining signal processing with intelligent algorithm. Firstly, the signal is decomposed by CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), and the decomposed IMFs (Intrinsic Mode Functions are obtained). Secondly, the filtered modal set is taken as input, and the features are extracted by using Convolutional Neural Networks (CNN) to get its deeper features. Finally, the obtained feature parameters are input into the support vector machine (SVM) optimized DBO:(Dung Beetle Optimizer) for classification. The experimental results show that CEEEMDAN-CNN-DBO-SVM model can effectively identify the types of acupoints, with an average accuracy rate of 93.01% at rest and 90.6% at click stimulation. The effect is better than CNN, SVM, CEEMDAN-CNN, CEEMDAN-SVM, CNN-SVM and other five classification methods.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Juan Tu, Zunxiang Dai, and Jianguo Chen "Acupoint classification method based on improved support vector machine", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030T (16 October 2023); https://doi.org/10.1117/12.3009557

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