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

• Autorzy: Hammachi Radhouane , Messaoudi Noureddine , Belkacem Samia , Pasetto Edoardo , Delilbasic Amer

Identyfikatory

DOI

10.2478/pjmpe-2025-0013

• Języki publikacji: EN

Abstrakty

EN

Introduction: Electromyography (EMG) analysis is one of the most fundamental approaches for diagnosing neuromuscular diseases. Current advancements in technology have the potential to improve diagnosis accuracy using artificial intelligence (AI). Quantum machine learning (QML), while still in its early stages, offers promising potential for various medical applications, but its effectiveness in real-world diagnostic tasks needs further exploration. Thus, the aim of this study is to employ both quantum and classical support vector machines (SVMs) to classify EMG signals into two classes, healthy and myopathy, and compare their performance. Methods: Various approaches were tested; classical SVM and quantum-kernel-based SVM, both with manually extracted features, and convolutional neural network (CNN)-based deep features extraction techniques. This allows for an evaluation of the strengths and limitations of this new technology, acknowledging the potential of both classical and quantum methods. Results: The obtained results showed that the proposed quantum methods yielded promising outcomes and comparable to classical methods. Particularly, the competitive results of the quantum SVM (QSVM) with the CNN-based deep feature extraction approach, which delivered a high training and testing accuracies of up to 96.7% and 85.1%, respectively. Conclusion: These findings encourages the necessity for more advanced QML research, particularly in medical applications as quantum technology progresses.

Słowa kluczowe

EN

electromyography; EMG; diagnosis; myopathy; quantum machine learning; QML; support vector machine; SVM

• Wydawca: Polskie Towarzystwo Fizyki Medycznej
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2025
• Tom: Vol. 31, Iss. 2
• Strony: 118--130
• Opis fizyczny: Bibliogr. 40 poz., rys., tab.

Twórcy

autor

Hammachi Radhouane

• LIST Laboratory, Faculty of Technology, UMBB, 35000 Boumerdes, Algeria
• Department of Electrical Systems Engineering, Faculty of Technology, UMBB, 35000 Boumerdes, Algeria

• https://orcid.org/0009-0001-5065-8260

autor

Messaoudi Noureddine

• LIST Laboratory, Faculty of Technology, UMBB, 35000 Boumerdes, Algeria
• Department of Electrical Systems Engineering, Faculty of Technology, UMBB, 35000 Boumerdes, Algeria

autor

Belkacem Samia

• Department of Electrical Systems Engineering, Faculty of Technology, UMBB, 35000 Boumerdes, Algeria

autor

Pasetto Edoardo

• Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
• RWTH Aachen University, 52056 Aachen, Germany

autor

Delilbasic Amer

• Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
• University of Iceland, Reykjavík, Iceland
• Φ-lab, ESRIN, European Space Agency, Italy

Bibliografia

• 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