Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals. (February 2018)

• Record Type: Journal Article
• Authors: Kręcisz, Krzysztof; Bączkowicz, Dawid
• Is Part Of: Computer methods and programs in biomedicine. Volume 154(2018)
• Page Start: 37
• Page End: 44
• Publication Date: 2018-02
• Subjects: Vibroarthrography -- Joint motion quality -- Machine learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.10.027
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We extend the VAG signal categorization to a multiclass classification, according to the various PFJ disorders and its stages, diagnosed by MRI, considered as the gold standard for PFJ chondral lesions. Using SimpleLogistic algorithm, we obtained 69% and 90% accuracy and AUC respectively, and sensitivity and specificity over 91% and 69%. Analysis and the classification of the VAG signals give satisfactory results for the screening and constitute a promising tool for classifying signals of various knee joint disorders and their stages. Abstract: Background and Objective: Vibroarthrography (VAG) is a method developed for sensitive and objective assessment of articular function. Although the VAG method is still in development, it shows high accuracy, sensitivity and specificity when comparing results obtained from controls and the non-specific, knee-related disorder group. However, the multiclass classification remains practically unknown. Therefore the aim of this study was to extend the VAG method classification to 5 classes, according to different disorders of the patellofemoral joint. Methods: We assessed 121 knees of patients (95 knees with grade I-III chondromalacia patellae, 26 with osteoarthritis) and 66 knees from 33 healthy controls. The vibroarthrographic signals were collected during knee flexion/extension motion using an acceleration sensor. The genetic search algorithm was chosen to select the most relevant features of the VAG signal for classification. Four different algorithms were used for classification of selected features: logistic regression with automatic attribute selection ( SimpleLogistic in Weka), multilayer perceptron with sigmoid activation function ( MultilayerPerceptron ), John Platt's sequential minimal optimization algorithm implementation of support vector classifier ( SMO ) and random forest tree ( RandomForest ). The generalization error of classification algorithms was evaluated by stratified 10-fold cross-validation. Results: We obtained levels of accuracy and AUC metrics over 90%, more than 93% sensitivity and more than 84% specificity for the logistic regression-based method (SimpleLogistic) for a 2-class classification. For the 5-class method, we obtained 69% and 90% accuracy and AUC respectively, and sensitivity and specificity over 91% and 69%. Conclusions: The results of this study confirm the high usefulness of quantitative analysis of VAG signals based on classification techniques into normal and pathological knees and as a promising tool in classifying signals of various knee joint disorders and their stages.