Electromyography Signal Classification With Artificial Intelligence for Detection of Neuromuscular Disorders Using a Large Clinically‐Acquired Database

作者：Mohamed Taha, Shuaiqi Huang, Xiaofeng Wang, Abdülhamit Subaşı, John A. Morren · 发表于：Muscle & Nerve · 年份：2025 · DOI：10.1002/mus.70087 · 被引用次数：3 · 研究领域：Muscle activation and electromyography studies、Transcranial Magnetic Stimulation Studies、Advanced Sensor and Energy Harvesting Materials

INTRODUCTION/AIMS: Artificial intelligence (AI) has shown potential in analyzing electromyography (EMG) signals, but clinical applicability remains limited by studies based on small, curated datasets, and variable accuracy. This study evaluated AI performance in classifying needle electromyography (EMG) signals as muscle activity versus background/noise/artifact, and then in distinguishing three clinical categories: amyotrophic lateral sclerosis (ALS), myopathy, and non-disease controls. METHODS: Data from the Cleveland Clinic Foundation EMG Database (CCFDB), a large clinically acquired EMG dataset was utilized for this study. A two-step classification approach was used: a convolutional neural network (CNN) to separate muscle activity from background/noise/artifact, followed by a random forest algorithm and CNNs for clinical category classification. Feature extraction techniques included Short-Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), and Wavelet Packet Decomposition (WPD). RESULTS: EMG data from 608 participants (266 ALS, 89 myopathy, 253 non-disease controls), totaling 11,456 muscle recordings, and 15,613 segments of muscle activity were included. The muscle activity detection model achieved 85.4% accuracy. For clinical category classification, CWT with a two-layer CNN performed best on the CCFDB (62% accuracy). Deeper CNN architectures did not consistently improve performance. On the publicly available curated EMGl...