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Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the preprocessing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
Electromyograpy (EMG) refers to the collective electric signal from muscles, which is controlled by the nervous system and produced during muscle contraction. The signal represents the anatomical and physiological properties of muscles; in fact, an EMG signal is the electrical activity of a muscle's motor units, which consist of two types: surface EMG, and intramuscular EMG [
The identity of an actual EMG signal that originates in the muscle is lost due to the mixing of various noise signals or artifacts. The attributes of the EMG signal depend on the internal structure of the subject, including the individual skin formation, blood flow velocity, measured skin temperatures, the tissue structure (muscle, fat,
All types of electronic equipment generate electrical noise, otherwise known as “inherent noise”. This noise has frequency components that range from 0 Hz to several thousand Hz. Two kinds of EMG signals in widespread use include surface EMG, and intramuscular (needle and finewire) EMG. To perform intramuscular EMG, a needle electrode or a needle containing two finewire electrodes is placed within the muscle of interest (invasive electrode). However, the use of surface electrodes has become more accepted in clinical and physiological applications [
For recording the EMG, the noninvasive electrodes are applied to the skin of the subject. For recording purposes, electrodes made of silver/silver chloride (10 × 1 mm) have been found to give adequate signaltonoise ratio and are electrically very steady. For this reason, they are widely used as surface electrodes [
Movement of the cable connecting the electrode to the amplifier and the interface between the detection surface of the electrode and the skin creates motion artifacts. Muscle fibers generate electric activity whenever muscles are active [
The human body behaves like an antenna—the surface of the body is continuously inundated with electric and magnetic radiation, which is the source of electromagnetic noise. Electromagnetic sources from the environment superimpose the unwanted signal, or cancel the signal being recorded from a muscle. The amplitude of the ambient noise (electromagnetic radiation) is sometimes one to three times greater than the EMG signal of interest.
The human body's surface continuously emits electromagnetic radiation, and avoiding exposure to ambient noise on the surface of the Earth is impracticable [
An undesired EMG signal from a muscle group that is not commonly monitored is called “crosstalk”. Crosstalk contaminates the signal and can cause an incorrect interpretation of the signal information [
Mezzarane
Anatomical, biochemical and physiological factors take place due to the number of muscle fibers per unit, depth and location of active fibers, and amount of tissue. These factors are called internal noise and directly affect EMG signal quality. Conventionally, physical capacitive effects are assumed negligible when analyzing the EMG signals. However, these assumptions might not be valid f or muscle tissue. Both muscle conductivity and permittivity are frequencydependent (dispersive). Furthermore, skin has a relatively low conductivity and high permittivity such that capacitive effects would be expected to be significant and the dispersive effects of permittivity will be more pronounced [
The amplitude of the EMG signal is quasirandom in nature. The frequency components between 0 and 20 Hz are mostly unstable because they are affected by the firing rate of the motor units. The firing rate of the motor units is quasirandom in nature. Because of the unstable nature of these components of the signal, it is considered as unwanted noise. The numbers of active motor units, motor firing rate and mechanical interaction between muscle fibers can change the behavior of the information in the EMG signal [
The electrical activity of the heart is the foremost interfering component for surface electromyography (sEMG) in the shoulder girdle, which is called an “electrocardiogram (ECG) artifact” [
In the field of clinical diagnosis and biomedics, the analysis of EMG signals with powerful and advanced methodologies is becoming more and more a required tool for healthcare providers. This overview covers recent advances in the field of EMG signal processing.
The timefrequency plane is one of the most fundamental concepts in signal analysis. The Wignerville distribution (WVD) is one timefrequency representation method, which is used for analyzing the EMG signal. In 1992, Ricamato
Wavelets have been growing in popularity as an alternative to the usual Fourier transform method. The wavelet transform can essentially be divided into discrete and continuous forms. It efficiently transforms the signals with a flexible resolution in both time and frequencydomains. The time taken for processing the signal using Discrete Wavelet Transform (DWT) method is low. However, in Continuous Wavelet Transform (CWT), it is more consistent and less timeconsuming due to the absence of down sampling. The DWT method has been successful in analyzing nonstationary signals, such as surface EMG (sEMG) signals, but it yields a highdimensional feature vector [
The basic analytical expression for CWT is presented in
Successive lowpass and high pass filtering in the discretetime domain computes the DWT. The general equation of DWT (
Daubechies analyzed the time series that contained nonstationary power at many different frequencies, by using wavelet transform [
The preprocessing stage based on a wavelet denoising algorithm for sEMG upper and lowerlimb movement recognitions has been a huge success over the past few years [
The benefit of using a wavelet basic function is that it has continuous derivatives, which allows it to decompose a continuous function more efficiently. It also avoids unwanted signals. Daubechies's wavelets provide better energy concentration with longlength filters than those with shortlength filters [
By investigating and analyzing various research studies on wavelet transform, the author has concluded that analyzing sEMG signals using Daubechies's function renders successful results. For obtaining better results from a sEMG analysis on different applications, the author recommends to use the db function (db2, db4, db6, db44 and db45) at decomposition level 4. In case of high and low noises in sEMG, the db function at decomposition level 4 can be used as a compromise level. The author simulated the raw sEMG signal by using the above wavelet functions.
Higher order spectra are defined as spectral representations of higher order cumulants of a random process. Let x (k) be a real, discrete time and nthorder stationary random process. Moreover, let w = [w1, w2…wn] T and
In practice, the nthorder moment can be equivalently calculated by taking an probability over the process multiplied by (n−1) lagged versions of itself. Higher order spectra are often estimated directly in the spectral domain as expected values of higher order periodograms. The spectral representation of Higher Order Statistics (HOS), such as moments and cumulants of the third order and above, are known as polyspectra or higher order spectra. For efficient processing of the EMG signal, HOS is applicable due to its unique properties. HOS can identify deviations from linearity, stationarity or Gaussianity in the signal [
Whenever a signalprocessing technique is applied on the diagnosis of neuromuscular disorders, some parameters, such as amplitude, number of phases, spike duration, number of turns,
EMD is a moderately new, datadriven adaptive technique for the analysis of nonstationary and nonlinear signals. EMD is a method to analyze the underlying notion of instantaneous frequency, and provides insight into the timefrequency signal features. The EMD method was first introduced by Huang
EMD aims to decompose a multicomponent signal, x(t) into a number of virtually monocomponent IMFs, h(t) plus a nonzeromean value of the residual component, r(t):
Each one of the IMFs; e.g., h(k + 1), is obtained by applying a process called sifting to the residual multicomponent signal as in the following
The sifting process is an iterative procedure which aims to achieve improved estimates of hk(t) in each iteration. More specifically, during the (n + 1) th sifting iteration, the temporal estimate of the IMF hnk(t), is obtained in the previous sifting iteration. This process is repeated until the designated IMF fulfills the following criteria:
The number of extrema and the number of zero crossings must either equal one another, or differ at most by one.
The mean value of the upper envelope and lower envelope is zero at any point of the whole time series.
When the IMF component is a monotonic function, the process is finalized and the original signal is reconstructed by adding all the IMF components along with the mean of final residue, m_{final}. Final residue is obtained by the difference between S(t) and the sum of all IMFs. The reconstructed signal, S(t) can be represented as in the following
During the signal processing, EMG signals use the EMD for background activity attenuation. EMD is very effective for noise reduction because it is a nonlinear method that can deal with nonstationary data. This procedure makes no assumptions about the input timeseries where the wavelet procedure depends on the basic mother wavelet function. Andrade
The major drawback of the EMD method is that it is more sensitive to the presence of noise, and has a modemixing problem. The EMD method is also a timeconsuming process. Therefore, a more robust, noiseassisted version of the EMD algorithm, called Ensemble EMD (EEMD) is used [
By studying the sEMG signal analysis using the empirical mode decomposition technique, the author has come to the conclusion that the EEMD method offers the most successful results for the attenuation of specific noises of sEMG signals. This method is more robust and the filtering procedure is able to directly extract signal components, which overlap significantly in time and frequency. EEMD achieved best surface EMG denoising performance for attenuating noises, especially in cases of powerline noises (PLI), white Gaussian noise (WGN), baseline wandering (BW) and ECG artifacts.
The Neural Network (NN) approach is suitable for modeling nonlinear data and is able to cover distinctions among different conditions. The requirements for designing an ANN for a given application include: (i) determining the network architecture; (ii) defining the total number of layers, the number of hidden units in the middle layers and number of units in the input and output layers; and (iii) the training algorithm used during the learning phase [
Among these models, it compared: MultiLayer Perceptron NN (MLP), Generalized Feed Forward NN (Gen FF), Modular NN (Mod NN), Jordan/Elman NN, and Recurrent Neural Network. The RBF network possesses several distinctive features, which makes it unique from other networks.
The general
Here,
Determining the number of neurons in the hidden layer is very crucial because the data learning capability in the RBF neural networks depends on its sufficiency [
An Artificial Neural Network is not a very common method for sEMG signal processing for noise reduction. However, in recent years several researchers have applied the different approaches of the ANN method to sEMG noise removal. By analyzing all of the approaches, the author recommends to use Jordan/Elman NN as a sEMG noise reduction approach. The advantage of the Jordan/Elman NN is that it is simple, speedy and is capable of generalization.
The ICA algorithm has rapidly become one of the most prominent signal processing techniques. The ICA is a statistical method, which can assume the original signal from the mixture signal. P. Comon first proposed this method [
In this figure s (t) are the sources. X (t) are the recordings sˆ (t) are the estimated sources, A is the mixing matrix, and W is the unmixing matrix. Without nonGaussianity, the estimation of the ICA model is not possible. ICA yields improvements above Principal Component Analysis (PCA), when signals do not display a Gaussian distribution [
Sources are independent at each time instant
Mixing matrix is linear and propagation delays of it are negligible
The sources are stationary and do not change with time
The signals are nonGaussian
The electromyographic (EMG) artifacts are statistically and mutually independent.
Consequently, ICA is a feasible method for source separation and decomposition of an EMG signal. Nowadays it is widely used to separate and remove noise sources from EMG and to decompose EMG signals into a maximum number of independent components. There are different types of ICA algorithms; some of them are used for processing the EMG signal, such as the Fast ICA algorithm, the Joint Approximate Diagonalization of Eigenmatrices (JADE), and the Infomax Estimation or maximum likelihood algorithm. The Fast ICA algorithm is a very popular method due to its simplicity, fast convergence and satisfactory results.
Hyvarinen introduced new contrast (or objective) functions for ICA based on the minimization of mutual information first [
In this section, the authors have reviewed some of the more prevalent approaches to ICA along with their potential benefits when applying them to EMG signals. The author has concluded that the ICAbased filtering procedure provides successful results in removing ECG artifacts and powerline noise (PLI), due to its largely independent signaltonoise ratio, and because of its subtle effects on frequency content.
Because of the various noises and artifacts detected among EMG signals, required information remains an amalgam inside the raw EMG signals. However, if these raw signals are used as an input in sEMG classification, the efficiency of the classifier decreases. To improve the performance of the classifier, researchers have been using different types of EMG features as an input to the classifier. To achieve optimal classification performance, the properties of EMG feature space (e.g., Maximum Class separability, robustness, and the computational complexity) should be taken into consideration [
An efficient means of classifying electromyography (EMG) signal patterns has been the interest of many researchers in the modern era. There are different types of classifiers, which are effectively used for different EMG applications, such as Artificial Neural Network (ANN), fuzzy classifier, Linear Discriminant Analysis (LDA), SelfOrganizing Map (SOM) and Support Vector Machines (SVM) [
The success of the electromyogram classification system highly depends on the quality of the selected and extracted features [
Many researchers have highlighted the neural network classifier in EMG pattern recognition because it can represent both linear and nonlinear relationships taken from data being modeled. ANNs are nonlinear statistical data modeling tools that are inspired by the structure of biological neural networks and that are able to process an EMG signal. Del and Park suggested that ANN is a suitable technique for realtime applications of EMG [
A new EMG pattern discrimination method, called the Recurrent LogLinearized Gaussian Mixture Network (RLLGMN), and based on the Hidden Markov Model (HMM), was proposed by Bu
Moreover, Wei
On the other hand, ICA is a feasible method for source separation and decomposition of surface electromyogram (sEMG). Naik
TDSEP is an ICA algorithm based on the simultaneous diagonalization of several timedelayed correlation matrices. From the table it is observed that it provided the best performance and gave an overall efficiency of 97%. Use of ICA alone is not suitable for sEMG due to the nature of sEMG distribution and order ambiguity. Naik
Fuzzy logic systems have more advantages for biosignal classification. Due to such biological signal characteristics as nonrepeatable and stochastic, fuzzy logic is an advantageous technique in biomedical signal classification. Fuzzy logic methods are superior to neural networkbased approaches because of their simplicity and insensitivity to overtraining. The insufficient number of patterns interferes with the current sEMG, which repeatedly deepens by the inaccuracy of the instrumentation and analytical system. In order to resolve these difficulties, Khezri
Based on the level of complexity and the change in hand movement and rate of precision, the ANFIS proves to be better than ANN.
The classification of electromyography (EMG) signals is also very important for detecting diseases. In clinical diagnosis, the simplicity, speed and reliability of classification are essential. The EMG signals from disabled patients or patients with different neurological diseases such as Parkinson's, Huntington's, Amyotrophic Lateral Sclerosis,
Subasi
In 2012, Christodouloua
Subasi and Kiymik used the timefrequency methods such as STFT, WignerVille Distribution (WVD) and Continuous Wavelet Transform (CWT), which have been used as preprocessing techniques. ICA was also used to reduce the dimension of feature vectors. Then, the extracted features of the EMG signal were used as an input to the Multilayer Perceptron Neural Network (MLPNN), which could be used to detect muscle fatigue. They showed that ANN with ICA separates EMG signals from healthy and fatigued muscles. By avoiding the spectral estimation, the problems of the conventional Fourier spectral variables deriving method is overcome by this method. Timefrequency methods do not assume quasistationarity or linearity in the order for this method to be appropriate for nonstationary signals. Muscle fatigue is automatically detected by this method [
Moreover, for classifying EMG signals, Sezgin used higher order spectra [
This study showed that several undesired signal sources (extrinsic factors, inherent noise in electronic equipment, motion artifacts, ambient noise) can be attenuated to a great extent by using an active electrode. However, this basic technique is not sufficient for the abovementioned noise elimination problem. Researchers have used different types of processing techniques for canceling these noises. Proper use of these techniques can increase EMG signal quality to where the signal becomes much more accurate, simple, reliable and steady. Based on the studies reviewed, the wavelet transform and higher order spectra employed in the processing (noise reduction and significant information extract) method are optimal.
The study also described the use of the electromyography pattern recognition method, which is very important in different applications, such as rehabilitation devices, prosthetic arm/leg control, assistive technology, symptom detection for neuromuscular disorder, and so on.
In case of a disease monitoring system, two major criteria are applicable—one is robustness and reliability, and another is accuracy of diagnosis. Based on these criteria, the SVM classifier (where multiscale Amplitude Modulation and Frequency Modulation (AMFM) histogram features are used as an input) is suggested for classifying electromyography signals. AMFM features can capture instantaneous variations in amplitude, frequency and phase of the electromyography signal. For realtime control of a robotic arm or leg, surface electromyographic (EMG) signal classification is also an important issue. On the other hand, if the number of EMG channels and features increases, the number of control commands of the classifier also increases. A large number of features (especially time domain and time scale feature vector) which extract the significant but different types of information from electromyography also provide improved classification results. Dimensionality reduction methods, Principle Component Analysis (PCA), and Linear Discriminant Analysis (LDA) methods are recommended if a huge number of features are used as input to the classifier. The main advantage of using the method is that the computational complexity of classifiers is allayed greatly. Dimensionality reduction methods should transform the data to a space vector with low dimensions and keep maximum information of the signal.
Furthermore, for increasing the classification accuracy, a combination of processing methods and pattern recognition techniques is strongly recommended. This combination method may be helpful to increase the classification accuracy without having to use too many muscle positions. The findings of this study are tabularized in
A raw EMG signal contains more important information regarding the nervous system in useless form. The aim of this paper was to give detailed information about clearing up commonly associated noises and artifacts from EMG signals, and to explore the various methodologies for analyzing the signals. This study emphasized the algorithms and methodologies used for detecting, processing and classifying EMG signals, and discussed their advantages and disadvantages. This comparison of methodologies will help researchers encounter the perfect method for analyzing EMG signals, which is required in medical and physiological applications, such as diagnosis of neurological problems, biomedical and biochemical research, prosthetic arm control and enduser applications. It is the hope of this study to derive a clear and concise view of EMG processing methods for removing noise and to initiate improvements on current pattern recognition techniques.
The author would like to thank and acknowledge the medical services of Teknologi Kasihatan dan Perubatan Research Group. This study was supported by the University Kebangsaan, Malaysia, under the Malaysia Research Fund through the MSc. Program (Grant No. 030102SF0703).
The authors declare no conflict of interest.
General block diagram of PLI cancelling system.
Raw EMG signal denoised by wavelet function (
Block diagram of Empirical Mode Decomposition [
Raw EMG data from right
The empirical mode decomposition of the electromyography signal from right
Blind source separation (BSS) block diagram [
Block diagram of the process of EMG classification system.
Six motions in the order of (
Schematic diagram of back propagation artificial neural networks (BPANN) with LevenbergMarquardt algorithm [
Structure of the fuzzy system with four inputs and one output [
Three types of EMG signals; here yaxis represents amplitude (μV) [
List of 324 wavelet functions from 15 wavelet families.
Haar  db1  1 
Daubechies  db2db45  2–45 
Coiflet  coif1coif5  46–50 
Morlet  morl  51 
Complex Morlet  cmor  52–147 
Discrete Meyer  dmey  148 
Meyer  meyr  149 
Mexican Hat  mexh  150 
Shannon  shan  151–200 
Frequency Bspline  fbsp  201–260 
Gaussian  gaus  261–267 
Complex Gaussian  cgaus  268–275 
Biorthogonal  bior  276–290 
Reverse Biorthogonal  rbio  291–305 
Symlet  sym  306–324 
Comparison of different types of Neural network [
 

01  MLP  05,05,07  0.627751035  0.0100858  0.02468346  0.00336508 
02  Gen FF  05,05,07  0.01897900  0.00467948  
03  Mod NN  05,05,07  0.636114324  0.0115398  0.02638402  0.00299289 
04  Jor/elman NN  05,05,07  0.627025792  0.00994905  0.025520535  0.003213602 
05  Recurrent NN  05,05,07  0.616395357  0.00997408  0.024154242  0.003366557 
06  RBF network  05,05,07  0.025991453  0.003341636 
Comparison of all the NN architectures on test dataset [
MLP  Tanh  Momentum  0.02501 (noise)  0.78114 (noise)  1,000  19.16  253 
0.02482 (EMG)  0.58433 (EMG)  
 
FTLRNN  Linear  Momentum  0.000067 (noise)  0.99950 (noise)  1,000  14  10 
0.000048 (EMG)  0.99939 (EMG)  
 
RBF  Linear  Levenberg Marquardt (LM)  0.02470 (noise)  0.78414 (noise)  1,000  8.3  293 
0.02482 (EMG)  0.58509 (EMG) 
Mathematical representation of widely used sEMG feature extraction methods.
Integrated EMG(IEMG) 

Here  
 
Mean Absolute Value (MAV) 

 
Modified Mean Absolute Value 1 (MMAV1) 

 
 
Modified Mean Absolute Value 2 (MMAV2) 

 
 
Simple Square Integral(SSI) 

 
Variance of EMG (VAR) 

 
Root Mean Square (RMS) 

 
Waveform Length (WL) 

 
Willison Amplitude (WAMP) 

 
 
Log detector (LOG) 

 
Slope Sign Change (SSC) 

 
 
Zero crossing (ZC) 

 
 
Multiscale amplitude modulation–frequency modulation (AM–FM) 

Here n = 1, 2,…M indexes the AM–FM components, a_{n} represents the nth instantaneous amplitude, and ϕ_{n} represents the nth instantaneous phase. Here, AM–FM components are extracted over a dyadic filter bank. 
Discrimination results of five subjects (A, B, C, D and E) [
RLLGMN Mean ± SD (%)  99.06 ± 0.00  89.32 ± 0.37  93.04 ± 0.11  93.49 ± 0.00  92.75 ± 0.00 
LLGMN (Mean ± SD (%))  94.00 ± 5.50  82.83 ± 0.00  88.50 ± 0.04  88.67 ± 0.15  89.26 ± 0.14 
BPNN (Mean ± SD (%))  73.41 ± 7.86  46.52 ± 12.3  44.20 ± 10.4  69.79 ± 9.97  69.17 ± 7.00 
Performance of four types ICA algorithm (percentage) for isometric hand gesture Identification [
Raw EMG  60%  60%  60%  60% 
Infomax  80%  80%  80%  80% 
JADE  85%  85%  85%  85% 
Fast ICA  90%  90%  90%  90% 
TDESP  97%  97%  97%  97% 
Performance comparison between ANFIS and ANNbased methods [
ANFIS  92%  94.67% 
ANN  86.6%  92.2% 
Performances of the ANN, SVM, LR, LDA and ELM learning machines [
ANN  32.25  1.18  98.20 
SVM  1.80  0.20  96.15 
LR  0.10  0.05  97.50 
LDA  0.09  0.04  97.25 
ELM  0.07  0.005  99.75 
Summary of different EMG classification system.
FFT  NN (Feature dimensionality reduction by (SimpleFLDA)  Recognize Wrist motion  FCR & FCU (Four electrode)  94%  Oyama and Mitsukura [ 
 
MAV, SSCs, and AR model coefficients of the signal, ZC  Adaptive Neurofuzzy interference system (ANFIS)  Six classes of hand movement  Extensor digitorum, ECR, PL and FCU  92%  Khezri & Jahed [ 
 
MAV,RMS, VAR, SD, ZC, SSC & WL  BPANN with LevenbergMarquardt training algorithm  Hand motion pattern  Hand  89.2%  Ahsan 
 
WPT  MLP(Feature dimensionality reduction by SOFM + PCA)  Multifunction myoelectric hand control  97%  Chu, J.U  
 
FFT  Fuzzy interference system (FIS)  Hand motion recognition for controlling Robot hand  Hand  90%  Uchida 
 
RMS  SVM  Eight classes of hand movement for realtime control of a robotic arm.  Flexor carpi radialis, FCU, 
92–98%  Shenoy 
 
RMS,Entropy  BPANN (Gradientdescent algorithm)  Four hand gestures recognition for humancomputer interaction  Forearm, Abductor 
97.5%  Rajesh 
 
ARM and EMG histogram  CKLM  Control of a multidegreesoffreedom prosthetic hand.  PL, EDC,FCU, FDS,FDP  93.54%  YiHung [ 
 
Entropy  Error backpropagation type neural networks  Six Motion discrimination  Forearm (four paired electrode)  90%  Tsuji 
 
Force information 
RLLGMN  Six motion discrimination  Forearm (six channel)    Nan Bu 
 
RMS  BPANN  Classify six different hand gestures  Flexor carpi radialis, FCU, FDS, 
99%  Naik 
 
AMFM  KNN  Classified neuromuscular disorder  58%  Christodouloua  

 
SOM  60%  

 
SVM  78%  
 
AR  WNN  Classified neuromuscular disorder  90.7%  Subasi  

 
FEBANN  88%  
 
Vector elements extracted by STFT  MLPNN with LevenbergMarquardt (LM) and gradient descent (GDA) algorithms (Feature dimensionality reduction by ICA).  Muscle fatigue detection  Right 
88.5%  Subasi A. 

 
SPWVD  90%  

 
CWT  91%  
 
 
Quadratic phase coupling (QPC)  Extreme Learning Machine Algorithm (ELM)  Classify the EMG signals (an aggressive action or a normal action)  Right biceps & triceps, Left biceps & triceps, right & left thigh, right & left hamstring (8 channel)  99.75%  Sezgin, N. [ 
 
AR  SVM  Diagnosis of neuromuscular diseases  Biceps and Hypothenar eminence    Güler 
IEMGIntegrated EMG, WPTWavelet packet Transform, FFTFastFourier Transform, STFTShort time Fourier Transform, SPWVDSmoothed PseudoWignerVille Distribution, CWT Continuous wavelet transform, ARAutoregressive analysis, MAVMean amplitude value, RMSRoot mean square, VARVariance value, SDStandard deviation, ZCZero crossing, SSCSlope Sign Changes, WLWave length, RECRecurrent Rate, PaccPower, WmaxWavelet coefficient, Samp EnEntropy, ARMAutoregressive model, FMNFrequency mean, FMDFrequency median, FRFrequency ratio, PL
Summary of most important methods.
WignerVille Distribution (WVD) 
WVD exhibits excellent localization properties. It is very noisy, which is the major limitation of this method.  
 
Wavelet Transform (WT) 
WT has the capability of multiresolution problem. It is able to deal with multicomponent signals because it is not affected by crossterm. The stationary signal is assumed, it is the main restriction of WT.  
 
Artificial Neural Network (ANN) 
ANN can represent both linear and nonlinear relationships. Exhibit mapping potentialities, it can learn to map a set of inputs to a set of outputs and precisely detect data. The complexity of the network structure increases if the number of input dimensions increases.  
 
Higher order statistics (HOS) 
HOS is very useful in the detection and characterization of nonlinearities of mechanisms that generate time series via phase relations of their harmonic components. The HOS characterizes the nonGaussianity in a signal very well because the HOS of Gaussian signals are statistically zero. It contains both amplitude and phase information. HOS are translation invariant.  
 
Empirical Mode Decomposition (EMD) 
EMD method is able to deal with nonstationary and nonlinear data. It can decompose any complicated time series data precisely. The main difficulties of the EMD method is to implement the best spline. EMD algorithm is very sensitive for noise. Enhanced empirical mode decomposition is noiseassisted version and it is more robust.  
 
Independent Component Analysis (ICA) 
Sources e.g independent component must be nonGaussian for ICA which is the fundamental restriction of this method. It is sensitive to highorder statistics in the data, not just the covariance matrix. It delivers a more probable set of data, which helps to locate the data concentration in ndimensional space.  
 
Fuzzy Logic 
It is very simple and is insensitive to over training. The most important characteristics of the fuzzy logic system is that it can tolerate a certain degree of contradiction in the data. 