Surface electromyography (sEMG) is a non-invasive tool that can aid physiological assessment of knee disorders towards clinical interventions. Machine Learning (ML) is widely used to classify sEMG data to help with early detection of knee disorders; however, the inherent noise and the high non-linearity of sEMG signals make pattern recognition a challenging task. This study aims to partly overcome these challenges with existing ML-based classifiers by denoising sEMG signals further via an innovative two-fold evolutionary approach. A novel Genetic Algorithm-based denoising approach is applied to sEMG data to decrease the search space for pattern-related classification. Thereafter, the proposed denoising technique is coupled with an ML-based classifier to improve the discrimination between physiological and pathophysiological knee functions from sEMG data by optimising its hyperparameters too. Thus, the novel evolutionary approach serves two purposes. Firstly, it further reduces noise in sEMG signals via a new GA-based denoising technique to concurrently maximise mutual information and minimise entropy; secondly, it also enables the optimisation of the classifier’s hyperparameters. The classification performance of the resulting hybrid algorithm was validated using sEMG data on 144 subjects (67 patients with knee disorders, 77 healthy subjects) and was found higher (ACC = 99.57%, 95% CI: 99.47–99.66; AUC = 1, 95% CI: 0.98–1) than that of similar ML algorithms and published studies. The hybrid algorithm achieved the highest classification performance by leveraging an evolutionary approach for effective denoising and hyperparameter optimisation, whilst retaining the lowest computational cost. Thus, the proposed evolutionary denoising ML-based classifier is deemed an accurate and reliable decision support system to aid the detection of knee disorders.

表面肌电图（sEMG）是一种非侵入性工具，可帮助对膝盖疾病进行生理评估以进行临床干预。机器学习（ML）被广泛用于对sEMG数据进行分类，以帮助及早发现膝盖疾病；然而，sEMG信号的固有噪声和高度非线性使模式识别成为一项艰巨的任务。这项研究旨在通过创新的二次进化方法进一步消除sEMG信号，部分克服现有基于ML的分类器所面临的挑战。一种新颖的基于遗传算法的去噪方法被应用于sEMG数据，以减少用于模式相关分类的搜索空间。之后，所提出的降噪技术与基于ML的分类器相结合，通过优化sEMG数据的超参数来改善生理和病理生理膝盖功能与sEMG数据之间的区别。因此，新颖的进化方法有两个目的。首先，它通过一种新的基于GA的降噪技术进一步降低了sEMG信号中的噪声，以同时最大化互信息和最小化熵；其次，它还可以优化分类器的超参数。使用sEMG数据对144名受试者（67名膝关节疾病患者，77名健康受试者）的sEMG数据进行了验证，从而验证了所得混合算法的分类性能，并且发现该结果更高（ACC = 99.57％，95％CI：99.47–99.66； AUC = 1，95 ％CI：0.98–1），比类似的ML算法和已发表的研究要高。混合算法通过利用一种进化方法来实现有效的降噪和超参数优化，从而获得了最高的分类性能，同时又保持了最低的计算成本。因此，提出的基于ML的进化降噪分类器被认为是有助于检测膝盖疾病的准确而可靠的决策支持系统。