A Machine Learning-based Surface Electromyography Topography Evaluation for Prognostic Prediction of Functional
Naifu Jiang1,2, Keith Dip-Kei Luk1, Yong Hu1,2
1Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
Study Design:
A retrospective study.
Objective:
The aim of this study was to investigate the feasibility and applicability of support vector machine (SVM) algorithm in classifying patients with LBP who would obtain satisfactory or unsatisfactory progress after the functional restoration rehabilitation program.
Summary Of Background Data:
Dynamic surface electromyography (SEMG) topography has demonstrated the potential use in predicting the prognosis of functional restoration rehabilitation for patients with low back pain (LBP). However, processing from raw SEMG topography to make prediction is not easy to clinicians.
Methods:
A total of 30 patients with nonspecific LBP were recruited and divided into "responding" and "non-responding" group according to the change of Visual analog pain rating scale and Oswestry Disability Index. Each patient received a 12-week functional restoration rehabilitation program. A normal database was calculated from a control group from 48 healthy participants. Root-mean-square difference (RMSD) was extracted from the recorded dynamic SEMG topography during symmetrical and asymmetrical trunk-movement. SVM and cross-validation were applied to the prediction based on the optimized features selected by the sequential floating forward selection (SFFS) algorithm.
Results:
RMSD feature parameters following rehabilitation in the "responding" group showed a significant difference (P < 0.05) with the one in the "nonresponding" group. The SVM classifier with Quadratic kernel based on SFFS-selected features showed the best prediction performance (accuracy: 96.67%, sensitivity: 100%, specificity: 93.75%, average area under curve [AUC]: 0.8925) comparing with linear kernel (accuracy: 80.00%, sensitivity: 85.71%, specificity: 75.00%, average AUC: 0.7825), polynomial kernel (accuracy: 93.33%, sensitivity: 92.86%, specificity: 93.75%, average AUC: 0.9675), and radial basis function (RBF) kernel (accuracy: 86.67%, sensitivity: 85.71%, specificity: 87.50%, average AUC: 0.7900).
Conclusion:
The use of SVM-based classifier of SEMG topography can be applied to identify the patient responding to functional restoration rehabilitation, which will help the healthcare worker to improve the efficiency of LBP rehabilitation.
Level Of Evidence:
3.
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