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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0301263

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 this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.