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Biomarker Design Using Combinations of Upper Back Muscles to Classify Chronic Neck Pain During Curvilinear Walking

In this study, a biomarker is designed to detect chronic neck pain (CNP) using electromyography (EMG) signals during curvilinear walking. Only 19 time-domain features from each EMG signal of 40 participants (20 asymptomatic, 20 with CNP) have been used in this study. A Neighborhood Component Analysis (NCA) approach has been used to determine the most significant features with the highest discriminative power for conducting the diagnosis. Then Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) are used to process selected features, resulting in acceptable accuracy compared to previous research works in this field. The SVM-NCA model has gained the best classification performance for all combinations of different muscle groups. Using a combination of four muscle groups, the NCA-SVM-based model with only 15 features has obtained the best classification performance (accuracy: 97.5%, sensitivity: 100%, specificity: 95%, precision:96%). While With 17 features, this methodology has yielded the best classification results using a combination of three muscle groups (accuracy: 97.5%, sensitivity: 95%, specificity: 100%, precision:100%). However, the best performance for two muscle group combinations has been achieved with 18 features (accuracy: 95%, sensitivity: 90%, specificity: 100%, precision:100%).