Mr. Jianguo Chen Profile

Jianguo Chen

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 17 October 2023 Paper

Acupoint classification method based on improved support vector machine

Juan Tu, Zunxiang Dai, Jianguo Chen

Proceedings Volume 12803, 128030T (2023) https://doi.org/10.1117/12.3009557

KEYWORDS: Feature extraction, Support vector machines, Convolution, Evolutionary algorithms, Convolutional neural networks, Linear filtering, Modal decomposition, Mathematical optimization, Interference (communication), Equipment

Read 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.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years