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Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis
Zhang,Qi1; Zhou,Jianhang2; Zhang,Bob3
2020-12
Source PublicationIEEE Journal of Biomedical and Health Informatics
ISSN2168-2194
Abstract

It is well known in Traditional Chinese Medicine (TCM) that a person's wrist pulse signal can reflect their health condition. Recently, many computerized wrist pulse AI systems have been proposed to simulate a practitioner's three fingers in order to acquire the wrist pulse signals (three positions/channels) from a candidate's wrist dynamically, before evaluating their health status based on the various feature extraction and detection methods. However, few works have investigated the correlation of the extracted features from the three wrist channels and comprehensively fused the various features together, which can improve the performance of wrist pulse diagnosis. In this paper, we propose a graph based multichannel feature fusion (GBMFF) method to utilize the multichannel features of the wrist pulse signals effectively. In detail, two different sensors, i.e., pressure and photoelectricity are used to capture the three channels of the wrist pulse signals. These are used to generate two different features by applying the stacked sparse autoencoder and wavelet scattering. Each feature of one wrist pulse sample is regarded as a node associated with its corresponding feature vector, and used to construct a graph for one candidate. A novel algorithm is implemented to construct different graphs for different candidates, which are used for wrist pulse diagnosis by developing graph convolutional networks. Experimental results indicate that our proposed AI-based method can obtain superior performances compared to other state-of-the-art approaches.

KeywordDiabetes Diseases Feature Extraction Feature Extraction Feature Fusion Graph Convolutional Networks Pulse Signal Scattering Sensors Testing Wrist Wrist Pulse Diagnosis
DOI10.1109/JBHI.2020.3045274
URLView the original
Language英語English
Scopus ID2-s2.0-85098761353
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Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang,Bob
Affiliation1.Department of Computer and Information Science, University of Macau, 59193 Taipa, Macao, (e-mail: yc07485@connect.um.edu.mo)
2.Computer and Information Science, University of Macau, 59193 Taipa, Macao, (e-mail: yc07424@um.edu.mo)
3.Computer and Information Science, University of Macau, 59193 Taipa, Macao, (e-mail: bobzhang@um.edu.mo)
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhang,Qi,Zhou,Jianhang,Zhang,Bob. Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis[J]. IEEE Journal of Biomedical and Health Informatics,2020.
APA Zhang,Qi,Zhou,Jianhang,&Zhang,Bob.(2020).Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis.IEEE Journal of Biomedical and Health Informatics.
MLA Zhang,Qi,et al."Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis".IEEE Journal of Biomedical and Health Informatics (2020).
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