EMG Signal Processing for Disease Classification using Machine Learning

Electromyography (EMG) plays a crucial role in the medical sector for assessing muscular health and diagnosing related ailments. With a growing global demand for EMG equipment and its surpassing significance over other diagnostic tools, EMG has become instrumental in addressing muscle disorders and designing prosthetic limbs. EMG captures electrical impulses generated during muscle activities, finding applications in medical, biological, and human-computer interface realms. Recent studies have delved deep into refining EMG signal processing through various machine learning algorithms, such as neural networks and support vector machines. This paper comprehensively reviews methodologies that include supervised learning, unsupervised learning, data preprocessing, feature extraction, classification algorithms such as real-time classification, and performance evaluation. The synthesis of cutting-edge techniques from interdisciplinary sectors promises a brighter future for EMG-based applications, particularly in assistive technologies. The writer has made effort for understanding better categorization of EMG signals for more significant research.