An Extreme Learning Machine Method for Diagnosis of Patellofemoral Pain Syndrome

Shi, Wuxiang; Xiong, Boli; Meilan, Huang; Du, Min; Yang, Yuan

doi:10.1007/978-3-030-58989-9_3

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…In order to further confirm the performance of our method, we compare it against the following five methods: Extreme Learning Machines (ELM), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Back Propagation Neural Network (BP) and Long Short-Term Memory (LSTM) (Shi et al, 2019;Shi et al, 2021b). Among them, the kernel function of the SVM is linear, the C-value is set to 0.04.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

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

Xiong

OuYang²,

Chang³

et al. 2022

Front. Neurosci.

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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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Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Among them, the kernel function of the SVM is linear, the C-value is set to 0.04. In the MLP, there is one hidden layer with 150 neurons trained for 3000 iterations (Shi et al, 2019). The ELM uses one hidden layer with 174 neurons.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%