American Journal of Computer Science and Information Technology

Recently, TQWT is one of the decomposition processes widely using in medical sciences as biomedical signal processing and vibration signals like seismic signals pro

. According to [19], by comparing with other discrete-time wavelet transforms, TQWT is very easy and efficient method in terms of tuning the parameters

. TQWT uses very efficient radix-2 FFT’s and filters having non-rational transfer functions like used in Fractional Spline Wavelet Transform (FSWT)

. In this study, TQWT decomposes the EMG signal into several high pass and low pass sub-bands

. In this paper, we are using two-channel filter banks in 7-stages to decompose EMG signal

. Each filter bank contains a low pass and a high pass filter with their respective scaling factors α and β

. The first level filter bank takes EMG signal as input whose sampling frequency is fs which will give two outputs comprises of a low pass and a high pass subband whose sampling frequencies are αfs and βfs respectively

. The low pass sub-band obtained from first stage filter bank will be the input for second level filter bank and it continues further upto 7 stages. 

American Journal of Computer Science and Information Technology

In this paper, tunable-Q factor wavelet transform (TQWT) based features are extracted for the classification of ALS and healthy EMG signals

. TQWT decomposes EMG signal into subbands and features such as mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy are extracted from the sub-bands

. The obtained statistical features are fed as inputs to k-NN and LS-SVM classifiers to classify ALS and healthy EMG signals

. The organization of the remaining paper is as follows: Section

. 2 contain, ALS and healthy EMG signals dataset, the proposed methodology- TQWT, and features extraction will present

. The results and discussions of classifying ALS and healthy EMG signals are in Section 3 and Section 4 conclude the paper

.The dataset comprises of 89 ALS and 133 healthy EMG signals which have been taken from, dataset R002 at http:// www.emglab.net [17]

. It consists of two groups namely ALS and healthy

. The healthy EMG dataset has been taken from 10 people of 21-37 years age, among them, females are 4 and males are 6, they didn’t have any kind of neuromuscular disorders

. ALS dataset has been taken from 8 patients among them 4 are females and 4 are males whose age is between 35-67 years

. Among the 8 patients taken, 5 have died within a few years

. A standard needle electrode has been used for accession

. Audio and Visual feedback has been used for monitoring the signal quality [18]

. The EMG signal has been amplified at a frequency range of 5 Hz-5 kHz filter settings and sampled at a frequency of 10 kHz

. Figure 1 shows the block diagram of proposed method explaining the whole method stepwise. Figure 2 shows the example of each of the ALS and healthy EMG signals.

American Journal of Computer Science and Information Technology

Discrete cosine transform (DCT) is used to extract the features from one of the MUAPs having a dynamic range, and the obtained feature set has been given as input to the k-Nearest Neighbor classifier (k-NN) to classify EMG signals [15]

. The coefficients obtained from autoregressive analysis have been given as input to neuro-fuzzy system for classifying EMG signals [16].

In this paper, tunable-Q factor wavelet transform (TQWT) based features are extracted for the classification of ALS and healthy EMG signals

. TQWT decomposes EMG signal into subbands and features such as mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy are extracted from the sub-bands

. The obtained statistical features are fed as inputs to k-NN and LS-SVM classifiers to classify ALS and healthy EMG signals

. The organization of the remaining paper is as follows: Section

. 2 contain, ALS and healthy EMG signals dataset, the proposed methodology- TQWT, and features extraction will present

. The results and discussions of classifying ALS and healthy EMG signals are in Section 3 and Section 4 conclude the paper.

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].

American Journal of Computer Science and Information Technology

Electromyography is recording and evaluating the electrical activities during contraction and relaxation of striated muscles
. The obtained electrical signal is called as electromyogram (EMG)
. EMG is being used in many fields such as exercise physiology
, human factors, etc., [1]
. In medical sciences, EMG is used for diagnosis of neuromuscular diseases based on the motor unit
. Motor unit is a combination of muscle fibers and motor neurons
. A motor neuron is a nerve cell originated in the spinal cord
. Upper motor neurons are situated at brain which sends signals to lower motor neurons at spinal cord to control the muscular activities
. Muscles present in eyes
, tongue, and the whole face are under the control of lower motor neurons
. The disorder of these motor neurons will create many diseases
, mostly amyotrophic lateral sclerosis (ALS) [1]
. It was first brought into international attention in 1939 because of Lou Gehrig’s speech at Yankee stadium
, and so this disease is also called with his name
. It results in the gradual degeneration and damage of nerve cells in the brain and spinal cord that controls the individual action of striated muscles
. It results in twitching
, cramping of muscles, slurred
, nasal speech
, and difficulty in chewing or swallowing
. ALS generally affects people around the age group of 65 years and 50 years in case of inherited patients [2]
. The manual analysis of ALS signals is a very monotonous task which requires professionals to diagnose
. An automatic, accurate, and fast method is required to diagnose the ALS EMG signals.

American Journal of Computer Science and Information Technology

Amyotrophic lateral sclerosis (ALS) is a disease, affects the nerve cells in brain and spinal cord that controls the voluntary action of muscles, which identification can be possible by processing electromyogram (EMG) signals

. This study focuses on the extraction of features based on tunable-Q factor wavelet transform (TQWT) for classifying ALS and healthy EMG signals

. TQWT decomposes EMG signal into sub-bands and these sub-bands are used for extraction of statistical features namely mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy

. The obtained features are tested on k-Nearest Neighbour and least squares support vector machines classifiers for the classification of ALS and healthy EMG signals

. The proposed method obtained better classification results as compared to other existing methods.