A Machine Learning-based Surface Electromyography Topography Evaluation for Prognostic Prediction of Functional Restoration Rehabilitation in Chronic Low Back Pain

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