# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 4 Conclusions  
Pattern classification is used in certain areas of knowledge. Depending on the needs and characteristics of each system, a specific tool may be selected. SVM offer a high classification accuracy since they allow the combination with other pattern classification methods to reach different objectives that are taken in the classification, besides a high accuracy percentage. In other words, it allows the incorporation of tools that transform the input data to the SVM or that solve the same.  
The mentioned authors combined methods to improve the classification accuracy in different applications, although the main purpose of the article is making a compilation of those studies which used myoelectric signals as input vector. In addition, other applications of classification based on SVM are listed to give the reader a broader idea of different fields of study in which the SVM can be applied. Then, four points can be concluded:  
* The most common kernel used was RBF, followed by linear and Gaussian.
* PCA is the most common tool for dimensionality reduction.
* MAV, SSC, ZC and WL are the most utilized time-domain features.
* Many channels are not necessary to obtain good precision.  
Most of the published papers seek advantage in feature extraction area, but only certain researchers reported an algorithm to combine directly with the SVM. An example of this is the combination with GA [39; 67; 70; 79]. Therefore, this is an area of opportunity, as there are several algorithms in artificial intelligence that can be tested and combined with SVM.  
Another small studied tool is, instead of using feature reduction algorithms such as PCA or SPCA, decreasing the number of input vectors when one has many features extracted; in such case, feature extraction algorithms can be used. Currently, these algorithms are mainly used in data mining.  
Conceptualization, D.C.T.-P. and J.R.-R.; Methodology, D.C.T.-P.; Software, D.C.T.-P.; Validation, D.C.T.-P. and J.C.J.-C.; Formal analysis, D.C.T.-P.; Investigation and Visualization, D.C.T.-P. and J.R.-R.; Data curation, D.C.T.-P., and J.C.J.-C.; Writing--original draft preparation, Writing--original draft, review & editing, all the authors.  
CONACYT paid for scholarship 561144 for this research.  
We would like to thank the Graduate Studies Division from the Faculty of Informatics at Universidad Autonoma de Queretaro by enabling [PERSON] to carry out this Ph.D. research.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AAC Average Amplitude Change  
AAV Average Absolute Value  
ACO Ant Colony Optimization  
AFB Amplitude of the First Burst  
ANN Artificial Neural Network  
AR Auto-Regressive  
CC Cepstrum Coefficients  
CBIR Content-Based Image Retrieval  
COF Coefficient of variation  
Com1 Mean value of all channels' mean values considered.  
Com2 RMS value of all channels' mean values considered.  
CWT Continuous Wavelet Transform  
DASDV Difference of Absolute Standard Deviation  
DCC Delta Cepstrum Coefficients  
DFT Discrete Fourier Transform  
DWT Discrete Wavelet Transform  
ECG Electrocardiogram  
EEG Electroencephalogram  
EGM Electrogram  
EMD Empirical Mode Decomposition  
EMG Electromyography  
ENT Entropy  
EOG Electroocologram  
EWC Electric WheelChair  
FD Frequency Domain  
FFT Fast Fourier Transform  
FMD Frequency Median Density  
FMN Frequency Mean Density  
FRD Fractal Dimension  
GA Genetic Algorithm  
HCI Human-Computer Interface  
HIST Histogram  
HMM Hidden Markov Models  
FR Frequency Ratio