Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid, Jordan.
PLoS One. 2024 May 7;19(5):e0302707. doi: 10.1371/journal.pone.0302707. eCollection 2024.
Knee osteoarthritis (OA) is a prevalent, debilitating joint condition primarily affecting the elderly. This investigation aims to develop an electromyography (EMG)-based method for diagnosing knee pathologies. EMG signals of the muscles surrounding the knee joint were examined and recorded. The principal components of the proposed method were preprocessing, high-order spectral analysis (HOSA), and diagnosis/recognition through deep learning. EMG signals from individuals with normal and OA knees while walking were extracted from a publicly available database. This examination focused on the quadriceps femoris, the medial gastrocnemius, the rectus femoris, the semitendinosus, and the vastus medialis. Filtration and rectification were utilized beforehand to eradicate noise and smooth EMG signals. Signals' higher-order spectra were analyzed with HOSA to obtain information about nonlinear interactions and phase coupling. Initially, the bicoherence representation of EMG signals was devised. The resulting images were fed into a deep-learning system for identification and analysis. A deep learning algorithm using adapted ResNet101 CNN model examined the images to determine whether the EMG signals were conventional or indicative of knee osteoarthritis. The validated test results demonstrated high accuracy and robust metrics, indicating that the proposed method is effective. The medial gastrocnemius (MG) muscle was able to distinguish Knee osteoarthritis (KOA) patients from normal with 96.3±1.7% accuracy and 0.994±0.008 AUC. MG has the highest prediction accuracy of KOA and can be used as the muscle of interest in future analysis. Despite the proposed method's superiority, some limitations still require special consideration and will be addressed in future research.
膝关节骨关节炎（OA）是一种常见的、使人虚弱的关节疾病，主要影响老年人。本研究旨在开发一种基于肌电图（EMG）的方法来诊断膝关节疾病。检查并记录了膝关节周围肌肉的 EMG 信号。该方法的主要组成部分包括预处理、高阶谱分析（HOSA）和通过深度学习进行诊断/识别。从一个公开可用的数据库中提取了正常膝关节和 OA 膝关节的个体在行走时的 EMG 信号。这项检查主要关注股四头肌、内侧腓肠肌、股直肌、半腱肌和股内侧肌。首先使用滤波和整流来消除噪声和平滑 EMG 信号。使用 HOSA 分析信号的高阶谱，以获取有关非线性相互作用和相位耦合的信息。最初，设计了 EMG 信号的双相干表示。将得到的图像输入到深度学习系统中进行识别和分析。使用经过改编的 ResNet101 CNN 模型的深度学习算法检查图像，以确定 EMG 信号是常规的还是表明膝关节骨关节炎。经过验证的测试结果表明，该方法具有很高的准确性和稳健的指标，表明该方法是有效的。内侧腓肠肌（MG）能够以 96.3±1.7%的准确率和 0.994±0.008 AUC 将膝骨关节炎（KOA）患者与正常人区分开来。MG 具有最高的 KOA 预测准确率，可以作为未来分析中的感兴趣肌肉。尽管该方法具有优势，但仍有一些局限性需要特别考虑，未来的研究将解决这些问题。
