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Classical and Quantum SVM for Electromyography-Based Myopathy Detection: A Comparative Exploration Cover

Classical and Quantum SVM for Electromyography-Based Myopathy Detection: A Comparative Exploration

Open Access
|Jun 2025

References

  1. Ibrahim I, Abdulazeez A. The Role of Machine Learning Algorithms for Diagnosing Diseases. J Appl Sci Technol Trends. 2021;2(01):10-19. doi:10.38094/jastt20179
  2. Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. Published online 2022. doi:10.1007/s12652-021-03612-z
  3. Čartolovni A, Tomičić A, Lazić Mosler E. Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. Int J Med Inform. 2022;161(December 2021). doi:10.1016/j.ijmedinf.2022.104738
  4. Garg A, Mago V. Role of machine learning in medical research: A survey. Comput Sci Rev. 2021;40:100370. doi:10.1016/j.cosrev.2021.100370
  5. Torres-Castilllo JR, López-López CO, Padilla-Castañeda MA. Neuromuscular disorders detection through time-frequency analysis and classification of multi-muscular EMG signals using Hilbert-Huang transform. Biomed Signal Process Control. 2022;71(January 2021). doi:10.1016/j.bspc.2021.103037
  6. Khamparia A, Singh A, Anand D, et al. A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders. Neural Comput Appl. 2020;32(15):11083-11095. doi:10.1007/s00521-018-3896-0
  7. Wei L, Liu H, Xu J, et al. Quantum machine learning in medical image analysis: A survey. Neurocomputing. 2023;525:42-53. doi:10.1016/j.neucom.2023.01.049
  8. Maheshwari D, Garcia-Zapirain B, Sierra-Sosa D. Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review. IEEE Access. 2022;10(July):80463-80484. doi:10.1109/ACCESS.2022.3195044
  9. Flöther FF. The state of quantum computing applications in health and medicine. Res Dir Quantum Technol. Published online 2023:1-15. http://arxiv.org/abs/2301.09106
  10. Khan TM, Robles-Kelly A. Machine Learning: Quantum vs Classical. IEEE Access. 2020;8:219275-219294. doi:10.1109/ACCESS.2020.3041719
  11. Tran A, Walsh CJ, Batt J, dos Santos CC, Hu P. A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profiles. J Transl Med. 2020;18(1):1-9. doi:10.1186/s12967-020-02630-3
  12. Tengshe R, Sharma A, Pandey H, Jayant GS, Pant L, Fatimah B. Automated Detection for Muscle Disease Using EMG Signal. Vol 606. Springer Nature Singapore; 2023. doi:10.1007/978-981-19-8563-8_16
  13. Cherifi D, Salah IS, Chihaoui T, Moudoud M, Boubchir L, Nait-Ali A. Automated Diagnosis of Neuromuscular Disorders using EMG Signals. In: 5th International Conference on Bio-Engineering for Smart Technologies (BioSMART ). IEEE; 2023:1-5. doi:10.1109/BioSMART58455.2023.10162039
  14. Kefalas M, Koch M, Geraedts V, Wang H, Tannemaat M, Back T. Automated Machine Learning for the Classification of Normal and Abnormal Electromyography Data. In: 2020 IEEE International Conference on Big Data, Big Data.; 2020:1176-1185. doi:10.1109/BigData50022.2020.9377780
  15. Abdel-maboud NF, Alfonse M. EMG SIGNAL CLASSIFICATION FOR NEUROMUSCULAR DISORDERS DIAGNOSIS USING TQWT AND BAGGING. Int J Intell Comput Inf Sci. 2023;23(3):19-30. doi:10.21608/ijicis.2023.195099.1256
  16. TUNCER E, DOĞRU BOLAT E. LSTM-based approach for Classification of Myopathy and Normal Electromyogram (EMG) Data. Balk J Electr Comput Eng. 2023;11(3):267-276. doi:10.17694/bajece.1228396
  17. Tannemaat MR, Kefalas M, Geraedts VJ, et al. Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach. Clin Neurophysiol. 2023;146:49-54. doi:10.1016/j.clinph.2022.11.019
  18. Afzal F, Khan MU, Faraz M, Naqvi SZH, Aziz S, Montes GA. Power of Cepstrum meets EMG: Detecting ALS and Myopathy. In: 2023 International Conference on Digital Futures and Transformative Technologies, ICoDT2. IEEE; 2023:1-6. doi:10.1109/ICoDT259378.2023.10325721
  19. Maheshwari D, Ullah U, Marulanda PAO, et al. Quantum Machine Learning Applied to Electronic Healthcare Records for Ischemic Heart Disease Classification. Human-centric Comput Inf Sci. 2023;13. doi:10.22967/HCIS.2023.13.006
  20. Ozpolat Z, Karabatak M. Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification. Diagnostics. 2023;13(6). doi:10.3390/diagnostics13061099
  21. Kumar Y, Koul A, Sisodia PS, et al. Heart Failure Detection Using Quantum-Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things. Wirel Commun Mob Comput. 2021;2021. doi:10.1155/2021/1616725
  22. Decoodt P, Liang TJ, Bopardikar S, et al. Hybrid Classical–Quantum Transfer Learning for Cardiomegaly Detection in Chest X-rays. J Imaging. 2023;9(7). doi:10.3390/jimaging9070128
  23. Umer MJ, Amin J, Sharif M, Anjum MA, Azam F, Shah JH. An integrated framework for COVID-19 classification based on classical and quantum transfer learning from a chest radiograph. Concurr Comput Pract Exp. 2022;34(20):1-14. doi:10.1002/cpe.6434
  24. Das R. Quantum Machine Learning based Computer Aided Diagnosis for Skin Cancer Detection: A Statistical Performance Analysis over Classical Approach. In: 2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT. IEEE; 2022:1-5. doi:10.1109/TQCEBT54229.2022.10041478
  25. Premanand V, B SSM, Srinivas S, Reddy S. Quantum Machine Learning for Breast Cancer Detection : A Comparative. Indian J Nat Sci. 2023;14(78):57728-57736.
  26. Khan MAZ, Innan N, Galib AAO, Bennai M. Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks. arXiv Prepr arXiv240115804. Published online 2024. http://arxiv.org/abs/2401.15804
  27. Gupta H, Varshney H, Sharma TK, Pachauri N, Verma OP. Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction. Complex Intell Syst. 2022;8(4):3073-3087. doi:10.1007/s40747-021-00398-7
  28. Rao GVE, B. R, Srinivasu PN, Ijaz MF, Woźniak M. Hybrid framework for respiratory lung diseases detection based on classical CNN and quantum classifiers from chest X-rays. Biomed Signal Process Control. 2024;88(PB):105567. doi:10.1016/j.bspc.2023.105567
  29. Gyongyosi L, Imre S. A Survey on quantum computing technology. Comput Sci Rev. 2019;31:51-71. doi:10.1016/j.cosrev.2018.11.002
  30. Henriet L, Beguin L, Signoles A, et al. Quantum computing with neutral atoms. Quantum. 2020;4:1-34. doi:10.22331/Q-2020-09-21-327
  31. Alexeev Y, Bacon D, Brown KR, et al. Quantum Computer Systems for Scientific Discovery. PRX Quantum. 2021;2(1):1. doi:10.1103/PRXQuantum.2.017001
  32. Nikolic M. Detailed Analysis of Clinical Electromyography Signals EMG Decomposition, Findings and Firing Pattern Analysis in Controls and Patients with Myopathy and Amytrophic Lateral Sclerosis. PhD Thesis, Faculty of Health Science, University of Copenhagen, 2001. [The data are available as dataset N2001 at http://www.emglab.net].
  33. Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst Appl. 2012;39(8):7420-7431. doi:10.1016/j.eswa.2012.01.102
  34. Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. Proc 2017 Int Conf Eng Technol ICET 2017. 2017;2018-Janua:1-6. doi:10.1109/ICEngTechnol.2017.8308186
  35. Wu H, Gu X. Towards dropout training for convolutional neural networks. Neural Networks. 2015;71:1-10. doi:10.1016/j.neunet.2015.07.007
  36. Hubregtsen T, Wierichs D, Gil-Fuster E, Derks PJHS, Faehrmann PK, Meyer JJ. Training quantum embedding kernels on near-term quantum computers. Phys Rev A. 2022;106(4):1-20. doi:10.1103/PhysRevA.106.042431
  37. Ciliberto C, Herbster M, Ialongo AD, et al. Quantum machine learning: A classical perspective. Proc R Soc A Math Phys Eng Sci. 2018;474(2209). doi:10.1098/rspa.2017.0551
  38. Schuld M, Killoran N. Quantum Machine Learning in Feature Hilbert Spaces. Phys Rev Lett. 2019;122(4):40504. doi:10.1103/PhysRevLett.122.040504
  39. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S. Quantum machine learning. Nature. 2017;549(7671):195-202. doi:10.1038/nature23474
  40. Preskill J. Quantum computing in the NISQ era and beyond. Quantum. 2018;2(July):1-20. doi:10.22331/q-2018-08-06-79
DOI: https://doi.org/10.2478/pjmpe-2025-0013 | Journal eISSN: 1898-0309 | Journal ISSN: 1425-4689
Language: English
Page range: 118 - 130
Submitted on: Sep 30, 2024
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Accepted on: Apr 17, 2025
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Published on: Jun 25, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Radhouane Hammachi, Noureddine Messaoudi, Samia Belkacem, Edoardo Pasetto, Amer Delilbasic, published by Polish Society of Medical Physics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.