Deep neural network assisted diagnosis of time-frequency transformed electromyograms

• Published in: Multimedia tools and applications Vol. 79; no. 15-16; pp. 11051 - 11067
• Main Authors: Bakiya, A., Kamalanand, K., Rajinikanth, V., Nayak, Ramesh Sunder, Kadry, Seifedine
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Classification; Classifiers; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Diagnosis; Electromyography; Fourier transforms; Frequency domain analysis; Multimedia Information Systems; Muscles; Neural networks; Particle swarm optimization; Special Purpose and Application-Based Systems; Electromyograms; Feature selection; Deep neural networks; Shallow neural networks; Transformation techniques; Time-frequency features

Electromyograms (EMG) are recorded electrical signals generated from the muscles and these signals are closely interrelated with the muscle activity and hence are useful for the investigation of neuro-muscular disorders. The feature mining, feature collection and development of classification system...