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

Baoping Xiong; Yaozong OuYang; Yiran Chang; Guoju Mao; Min Du; Bijing Liu; Yong Xu

doi:10.3389/fnins.2022.976249

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

Front Neurosci. 2022 Jul 29:16:976249. doi: 10.3389/fnins.2022.976249. eCollection 2022.

Authors

Baoping Xiong^{1

2}, Yaozong OuYang^{1

2}, Yiran Chang^{1

2}, Guoju Mao^{1

2}, Min Du³, Bijing Liu⁴, Yong Xu^{1

2}

Affiliations

¹ School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, China.
² Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China.
³ Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, Wuyi University, Wuyishan, China.
⁴ State Grid Electric Power Research Institute, Beijing, China.

Abstract

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.

Keywords: auxiliary diagnostic; fuse biometrics; graph convolutional neural network; patellofemoral pain syndrome; semi-supervised classification.