Introduction: Electromyography (EMG) analysis is one of the most fundamental approaches for diagnosing neuromus-cular diseases. Current advancements in technology have the potential to improve diagnosis accuracy using artificialintelligence (AI). Quantum machine learning (QML), while still in its early stages, offers promising potential for variousmedical applications, but its effectiveness in real-world diagnostic tasks needs further exploration. Thus, the aim of thisstudy 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 extractedfeatures, and convolutional neural network (CNN)-based deep features extraction techniques. This allows for an evalua-tion of the strengths and limitations of this new technology, acknowledging the potential of both classical and quantummethods.Results: The obtained results showed that the proposed quantum methods yielded promising outcomes and comparableto classical methods. Particularly, the competitive results of the quantum SVM (QSVM) with the CNN-based deep fea-ture 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 applica-tions as quantum technology progresses.
Hammachi et al. (Wed,) studied this question.