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

Polish Journal of Medical Physics And Engineering, vol. 31, pp. 118–130

Abstract

Abstract 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.

Authors 5

1. Radhouane Hammachi

   University of Boumerdes

   Affiliation as printed

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

   LIST Laboratory, Faculty of Technology, UMBB , Boumerdes , Algeria

2. Noureddine Messaoudi

   University of Boumerdes

   Affiliation as printed

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

   LIST Laboratory, Faculty of Technology, UMBB , Boumerdes , Algeria

3. Samia Belkacem

   University of Boumerdes

   Affiliation as printed

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

4. Edoardo Pasetto Aachen

   Forschungszentrum Jülich · Jülich Supercomputing Centre · RWTH Aachen University

   Affiliation as printed

   Jülich Supercomputing Centre , Forschungszentrum Jülich, 52428 Jülich , Germany

   RWTH Aachen University , Aachen , Germany

5. Amer Delilbasic

   University of Iceland · Forschungszentrum Jülich · European Space Agency · European Space Research Institute · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre , Forschungszentrum Jülich, 52428 Jülich , Germany

   University of Iceland , Reykjavík , Iceland ; and Φ-lab, ESRIN, European Space Agency , Italy