000			02583nam a22001577a 4500
082			_a610
100			_aShabbir, Nida _9132235
245			_aHigh-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain / _cNida Shabbir
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a94p. _bSoft Copy _c30cm
500			_aLow 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.
650			_aMS Biomedical Engineering (BME) _9119509
700			_aSupervisor: Dr. Muhammad Asim Waris _9119524
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/56476
942			_2ddc _cTHE
999			_c615492 _d615492