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A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome

Patellofemoral pain syndrome (PFPS) is a common, yet misunderstood, knee pathology. Early accurate diagnosis can help avoid the deterioration of the disease. However, the existing intelligent auxiliary diagnosis methods of PFPS mainly focused on the biosignal of individuals but neglected the common...

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Autores principales: Xiong, Baoping, OuYang, Yaozong, Chang, Yiran, Mao, Guoju, Du, Min, Liu, Bijing, Xu, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372351/
https://www.ncbi.nlm.nih.gov/pubmed/35968371
http://dx.doi.org/10.3389/fnins.2022.976249
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author Xiong, Baoping
OuYang, Yaozong
Chang, Yiran
Mao, Guoju
Du, Min
Liu, Bijing
Xu, Yong
author_facet Xiong, Baoping
OuYang, Yaozong
Chang, Yiran
Mao, Guoju
Du, Min
Liu, Bijing
Xu, Yong
author_sort Xiong, Baoping
collection PubMed
description Patellofemoral pain syndrome (PFPS) is a common, yet misunderstood, knee pathology. Early accurate diagnosis can help avoid the deterioration of the disease. However, the existing intelligent auxiliary diagnosis methods of PFPS mainly focused on the biosignal of individuals but neglected the common biometrics of patients. In this paper, we propose a PFPS classification method based on the fused biometrics information Graph Convolution Neural Networks (FBI-GCN) which focuses on both the biosignal information of individuals and the common characteristics of patients. The method first constructs a graph which uses each subject as a node and fuses the biometrics information (demographics and gait biosignal) of different subjects as edges. Then, the graph and node information [biosignal information, including the joint kinematics and surface electromyography (sEMG)] are used as the inputs to the GCN for diagnosis and classification of PFPS. The method is tested on a public dataset which contain walking and running data from 26 PFPS patients and 15 pain-free controls. The results suggest that our method can classify PFPS and pain-free with higher accuracy (mean accuracy = 0.8531 ± 0.047) than other methods with the biosignal information of individuals as input (mean accuracy = 0.813 ± 0.048). After optimal selection of input variables, the highest classification accuracy (mean accuracy = 0.9245 ± 0.034) can be obtained, and a high accuracy can still be obtained with a 40% reduction in test variables (mean accuracy = 0.8802 ± 0.035). Accordingly, the method effectively reflects the association between subjects, provides a simple and effective aid for physicians to diagnose PFPS, and gives new ideas for studying and validating risk factors related to PFPS.
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spelling pubmed-93723512022-08-13 A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome Xiong, Baoping OuYang, Yaozong Chang, Yiran Mao, Guoju Du, Min Liu, Bijing Xu, Yong Front Neurosci Neuroscience Patellofemoral pain syndrome (PFPS) is a common, yet misunderstood, knee pathology. Early accurate diagnosis can help avoid the deterioration of the disease. However, the existing intelligent auxiliary diagnosis methods of PFPS mainly focused on the biosignal of individuals but neglected the common biometrics of patients. In this paper, we propose a PFPS classification method based on the fused biometrics information Graph Convolution Neural Networks (FBI-GCN) which focuses on both the biosignal information of individuals and the common characteristics of patients. The method first constructs a graph which uses each subject as a node and fuses the biometrics information (demographics and gait biosignal) of different subjects as edges. Then, the graph and node information [biosignal information, including the joint kinematics and surface electromyography (sEMG)] are used as the inputs to the GCN for diagnosis and classification of PFPS. The method is tested on a public dataset which contain walking and running data from 26 PFPS patients and 15 pain-free controls. The results suggest that our method can classify PFPS and pain-free with higher accuracy (mean accuracy = 0.8531 ± 0.047) than other methods with the biosignal information of individuals as input (mean accuracy = 0.813 ± 0.048). After optimal selection of input variables, the highest classification accuracy (mean accuracy = 0.9245 ± 0.034) can be obtained, and a high accuracy can still be obtained with a 40% reduction in test variables (mean accuracy = 0.8802 ± 0.035). Accordingly, the method effectively reflects the association between subjects, provides a simple and effective aid for physicians to diagnose PFPS, and gives new ideas for studying and validating risk factors related to PFPS. Frontiers Media S.A. 2022-07-29 /pmc/articles/PMC9372351/ /pubmed/35968371 http://dx.doi.org/10.3389/fnins.2022.976249 Text en Copyright © 2022 Xiong, OuYang, Chang, Mao, Du, Liu and Xu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Xiong, Baoping
OuYang, Yaozong
Chang, Yiran
Mao, Guoju
Du, Min
Liu, Bijing
Xu, Yong
A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome
title A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome
title_full A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome
title_fullStr A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome
title_full_unstemmed A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome
title_short A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome
title_sort fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372351/
https://www.ncbi.nlm.nih.gov/pubmed/35968371
http://dx.doi.org/10.3389/fnins.2022.976249
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