EMG Signal Evaluation by Graph Signal Processing & Total Variation Denoising / (Record no. 608816)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02405nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Duaa, Iqra |
| 245 ## - TITLE STATEMENT | |
| Title | EMG Signal Evaluation by Graph Signal Processing & Total Variation Denoising / |
| Statement of responsibility, etc. | Iqra Duaa |
| 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 | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 63p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Electromyography (EMG) serves as a vital diagnostic tool in medical and clinical research,<br/>enabling the monitoring and analysis of muscle electrical activity. In medical diagnostics,<br/>EMG aids in identifying and assessing neuromuscular syndromes, i.e. amyotrophic lateral<br/>sclerosis (ALS). However, EMG signals are prone to various forms of noise and<br/>interference, posing challenges to accurate data interpretation. Thus, the development of<br/>robust denoising techniques is crucial for enhancing EMG signal quality and addressing<br/>practical challenges in clinical diagnostics, rehabilitation, and neuromuscular research.<br/>This research introduces an innovative methodology integrating Variational Mode<br/>Decomposition (VMD) and Graph Signal Processing (GSP) to improve EMG signal<br/>quality. Unlike conventional approaches like Continuous Wavelet Transform (CWT), this<br/>study explores the untapped potential of VMD with Intrinsic Mode Functions (IMFs) 16<br/>and GSP in EMG signal analysis. sEMG data collected from 10 subjects using the EMGUSB (OT Bioelettronica) underwent denoising techniques, specifically CWT, VMD, and<br/>GSP. Evaluation of noise reduction performance reveals compelling results, with GSP<br/>demonstrating superior noise reduction capabilities compared to VMD and CWT.<br/>Specifically, GSP increases the SNR by 259.15 meanwhile decreases the RMSE by 0.07.<br/>In comparison, VMD upturns SNR with 111.56 and declines RMSE of 0.15. While both<br/>VMD and GSP outperform CWT, which exhibits SNR enhancements of 90.46 and RMSE<br/>reductions by 0.15. Statistical analysis validates the significant improvements (p < 0.05)<br/>provided by VMD and GSP over CWT across varying noise levels. Notably, VMD and<br/>GSP collectively exhibit substantial enhancements in both SNR and RMSE metrics,<br/>underscoring their efficacy in preserving signal fidelity while minimizing noise and<br/>artifacts. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Mechanical Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Rehan Zahid |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/42889">http://10.250.8.41:8080/xmlui/handle/123456789/42889</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 | 04/15/2024 | 621 | SMME-TH-1009 | Thesis |