• TPC-GCN: Deep learning for pul...

Treffer: TPC-GCN: Deep learning for pulse pattern classification in traditional Chinese medicine.

Title:
TPC-GCN: Deep learning for pulse pattern classification in traditional Chinese medicine.
Authors:
Li H; State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan, 030051, Shanxi, China., Li Y; State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan, 030051, Shanxi, China., Zhang Z; State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan, 030051, Shanxi, China. Electronic address: zdzhang@nuc.edu.cn., Xue C; State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan, 030051, Shanxi, China., Li Z; State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan, 030051, Shanxi, China., Li X; College of Basic Medical Science, Shanxi University of Chinese Medicine, Jinzhong, 030619, Shanxi, China., Men J; College of Basic Medical Science, Shanxi University of Chinese Medicine, Jinzhong, 030619, Shanxi, China.
Source:
Medical engineering & physics [Med Eng Phys] 2025 Oct; Vol. 144, pp. 104401. Date of Electronic Publication: 2025 Jul 23.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Butterworth-Heinemann Country of Publication: England NLM ID: 9422753 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4030 (Electronic) Linking ISSN: 13504533 NLM ISO Abbreviation: Med Eng Phys Subsets: MEDLINE
Imprint Name(s):
Publication: London : Butterworth-Heinemann
Original Publication: Oxford, UK : Butterworth-Heinemann, c1994-
MeSH Terms:
Medicine, Chinese Traditional*/methods , Pulse Wave Analysis*/classification , Deep Learning* , Pulse*, Humans ; Convolutional Neural Networks ; Pattern Analysis, Machine
Contributed Indexing:
Keywords: Pulse classification; Pulse diagnosis; Pulse wave analysis
Entry Date(s):
Date Created: 20250909 Date Completed: 20250911 Latest Revision: 20250911
Update Code:
20260130
DOI:
10.1016/j.medengphy.2025.104401
PMID:
40925696
Database:
MEDLINE

Weitere Informationen

Pulse diagnosis holds a pivotal role in traditional Chinese medicine (TCM) diagnostics, with pulse characteristics serving as one of the critical bases for its assessment. Accurate classification of these pulse pattern is paramount for the objectification of TCM. This study proposes an enhanced SMOTE approach to achieve data augmentation, followed by multi-domain feature extraction. Graph data structures with varying configurations are subsequently constructed to facilitate more profound insights into the intrinsic information within the data. Additionally, a multi-channel lightweight graph convolutional network (GCN) is devised. This network's core strategy lies in extracting diverse layers of information through parallel branches, integrating local structural information with adaptive weights, and employing attention-weighted fusion to improve classification accuracy and model robustness. The proposed network model achieved 91.68% accuracy, a mean F1 score of 92%, a mean recall rate of 92%, and a mean precision rate of 92% on the pulse dataset. The results demonstrate a marked improvement in pulse classification accuracy, validating the efficacy of this approach while offering new perspectives and methodologies for pulse signal classification research.
(Copyright © 2025 IPEM. Published by Elsevier Ltd. All rights reserved.)

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.