High-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain / (Record no. 615492)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02583nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Shabbir, Nida |
| 245 ## - TITLE STATEMENT | |
| Title | High-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain / |
| Statement of responsibility, etc. | Nida Shabbir |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 94p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Low back pain (LBP) is one of the most prevalent musculoskeletal disorders worldwide,<br/>with diagnosis often relying on subjective evaluation rather than objective physiological<br/>measures. This thesis introduces a data-driven framework for quantitative assessment of<br/>LBP using high-density surface electromyography (HD-sEMG). As a non-invasive<br/>technique, HD-sEMG provides insight into spinal neuromuscular behavior, enabling<br/>spatial and temporal characterization of muscle activity patterns associated with<br/>dysfunction. The study recruited 39 participants, divided into three groups that are healthy,<br/>sub-clinical, and LBP based on chiropractic evaluation. In the first study, machine learning<br/>classifiers including Support Vector Machine (SVM), eXtreme Gradient Boosting<br/>(XGBoost), and Artificial Neural Network (ANN) were trained on time and frequencydomain features to discriminate between groups. The SVM model achieved the highest<br/>accuracy, effectively distinguishing subtle neuromuscular differences between healthy and<br/>dysfunctional subjects. In the second study, a regression-based framework was developed<br/>to predict vertebral joint dysfunction scores (C1-Sacral) derived from chiropractic<br/>assessment. ANN and Convolutional Neural Network (CNN) models were trained under a<br/>CORAL (Consistent Rank Logits) ordinal regression framework, preserving the ordinal<br/>nature of dysfunction severity. The ANN model demonstrated superior predictive<br/>performance, capturing non-linear relationships between HD-sEMG activity and graded<br/>dysfunction levels. Overall, this research bridges the gap between clinical assessment and<br/>computational diagnostics, showing that HD-sEMG signatures can objectively quantify<br/>spinal dysfunction and support data-driven LBP diagnosis. The proposed framework<br/>establishes a foundation for personalized rehabilitation, automated dysfunction mapping,<br/>and AI-assisted musculoskeletal diagnostics, advancing the integration of biomedical<br/>signal processing with clinical neurophysiology. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor: Dr. Muhammad Asim Waris |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/56476">http://10.250.8.41:8080/xmlui/handle/123456789/56476</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 12/12/2025 | 610 | SMME-TH-1198 | Thesis |