High-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain /
Nida Shabbir
- 94p. Soft Copy 30cm
Low back pain (LBP) is one of the most prevalent musculoskeletal disorders worldwide, with diagnosis often relying on subjective evaluation rather than objective physiological measures. This thesis introduces a data-driven framework for quantitative assessment of LBP using high-density surface electromyography (HD-sEMG). As a non-invasive technique, HD-sEMG provides insight into spinal neuromuscular behavior, enabling spatial and temporal characterization of muscle activity patterns associated with dysfunction. The study recruited 39 participants, divided into three groups that are healthy, sub-clinical, and LBP based on chiropractic evaluation. In the first study, machine learning classifiers including Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were trained on time and frequencydomain features to discriminate between groups. The SVM model achieved the highest accuracy, effectively distinguishing subtle neuromuscular differences between healthy and dysfunctional subjects. In the second study, a regression-based framework was developed to predict vertebral joint dysfunction scores (C1-Sacral) derived from chiropractic assessment. ANN and Convolutional Neural Network (CNN) models were trained under a CORAL (Consistent Rank Logits) ordinal regression framework, preserving the ordinal nature of dysfunction severity. The ANN model demonstrated superior predictive performance, capturing non-linear relationships between HD-sEMG activity and graded dysfunction levels. Overall, this research bridges the gap between clinical assessment and computational diagnostics, showing that HD-sEMG signatures can objectively quantify spinal dysfunction and support data-driven LBP diagnosis. The proposed framework establishes a foundation for personalized rehabilitation, automated dysfunction mapping, and AI-assisted musculoskeletal diagnostics, advancing the integration of biomedical signal processing with clinical neurophysiology.