Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders

By Abdulhamit Subasi. Comput. Biol. Medicine, 2013

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A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions (2016, methodology): “This method resulted in 73% accuracy, meanwhile, 96.75% [22] were gained by hybridizing the particle swarm optimization (PSO) and SVM in detecting neuromuscular disorders.”
Congestive heart failure detection using random forest classifier (2016, methodology): “However, the accuracy of the ECG signal classification may be affected by numerous different factors, like the quality of the signals obtained, performance of the feature extraction algorithm as well as the subsets used for purpose of training and testing [6].”
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (2015, methodology): “Subasi [321] proposed a novel PSO-SVM model that hybridized the PSO and SVM in order to improve the electromyography (EMG) signal classification accuracy.”