American Journal of Computer Science and Information Technology

The feature extraction process plays an important role for analysis and classification of EMG signals

. The various time, frequency, and time-frequency domain features have been used for classifying EMG signals

. Time domain features like autocorrelation, square integral, mean absolute value, variance and integrated EMG have been used for classification of ALS and healthy EMG signal [3,4]

. Frequency domain features like total power, peak frequency, median frequency, and mean power have been explored for classification of ALS and healthy EMG signals [5

]. Time-frequency domain features like root mean square (RMS), turns ratio, autoregressive coefficients have been used for classification of EMG signals [6]

. The classification of ALS and healthy EMG signals has also been done through short time Fourier transform (STFT) [7]

.Motor Unit Action Potentials (MUAPs) analysis and feature extraction was used with binary-support vector machine (SVM) classifier for the discrimination of ALS and healthy EMG signals [8]

. Time-domain based features like autoregression (AR), waveform length, and mean absolute value were given as inputs to multilayer perceptron (MLP) and artificial neural network (ANN) classifiers for classifying ALS and healthy EMG signals [9]

. The ensemble empirical mode decomposition (EEMD) decomposes EMG signal into set of intrinsic mode functions (IMFs), where the noisy IMFs separated using fast independent component analysis (ICA) algorithm and time domain features set used as input to linear discriminant analysis classifier for classification of EMG signals [10]

. Mel-frequency cepstral coefficients (MFCCs) based methods were used for the extraction of features from higher order MUAPs of an EMG signal, the obtained feature set have been applied to the knearest neighbour (k-NN) classifier for classifying ALS and healthy EMG signals [11]

. Spectral features like power spectral density, amplitude modulated bandwidth, and frequency modulated bandwidth were extracted by using improved empirical mode decomposition (IEMD) for classifying EMG signals [12]

. A new hybridization of SVM with particle swarm optimisation (PSO) as PSO-SVM used for classifying EMG signals [13]

. Multiscale principal component analysis (MSPCA) and discrete wavelet transform (DWT) have been used to extract the features and de-noising the EMG signal. The obtained features are applied to decision tree algorithms for classifying EMG signals [14].