S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality

• Published in: PloS one Vol. 19; no. 5; p. e0301263
• Main Authors: Vijayvargiya, Ankit, Sinha, Aparna, Gehlot, Naveen, Jena, Ashutosh, Kumar, Rajesh, Moran, Kieran
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31.05.2024; Public Library of Science (PLoS)
• Subjects: Abnormalities; Adult; Algorithms; Artificial intelligence; Classifiers; Data processing; Decomposition; Diagnosis; Electromyography; Electromyography - methods; Female; Humans; Injuries; Knee; Knee - physiopathology; Knee Joint - physiopathology; Learning algorithms; Machine Learning; Male; Medical diagnosis; Muscles; Neural networks; Neuromuscular diseases; Orthopedics; Oversampling; Range of motion; Signal analysis; Signal classification; Signal processing; Signal Processing, Computer-Assisted; Support vector machines; Wavelet Analysis; Wavelet transforms; Taiwan

The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address th...