Classification of EMG Signals Using ANFIS for the Detection of Neuromuscular Disorders

Abstract

Electromyography is used as a diagnostic tool for detecting different neuromuscular diseases and it is also a research tool for studying kinesiology which is the study of human- and animal-body muscular movements. Electromyography techniques can be employed with the diagnosis of muscular nerve compression and expansion abnormalities and other problems of muscles and nervous systems. An electromyogram (EMG) signal detects the electrical potential activities generated by muscle cells. These cells are activated by electrochemical signals and neurological signals. It is so difficult for the neurophysiologist to distinguish the individual waveforms generated from the muscle. Thus, the classification and feature extraction of the EMG signal becomes highly necessary. The principle of independent component analysis (ICA), fast Fourier transform (FFT) and other methods is used as dimensionality reduction methods of different critical signals extracted from human body. These different existing techniques for analysis of EMG signals have several limitations such as lower recognition rate waveforms, sensitive to continuous training and poor accuracy. In this chapter, the EMG signals are trained using soft computing techniques like adaptive neuro-fuzzy inference system (ANFIS). ANFIS is the hybrid network where fuzzy logic principle is used in neural network. This proposed technique has different advantages for better training of the EMG signals using ANFIS network with a higher reliability and better accuracy. Discrete wavelet transformation (DWT) method is used for feature extraction of the signal.