# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 3 Myoelectric Signal Classification
### SVM-Based Myoelectric Signal Classification
The classification of other types of signals has also been utilized as a guide for some doctors and therapists for the detection of different diseases, or the diagnosis of disorders in the motor system. In [65], an adaptive system for SVM, called ASVM, is proposed to diagnose diseases through the blood, using data on diabetes and breast cancer. In this method, the bias value of SVM is adjusted by a feedback mechanism, which allows the classification to be done more quickly and with higher precision than in its different evaluation, obtaining 67.22-97.39% accuracy. Similarly, features in temporal and frequency space were utilized in [66] to perform the detection of muscular fatigue of the lower extremities to prevent falls and injuries; with the aid of six cameras, the authors differentiated between the state of fatigue and without fatigue using SVM with linear and RBF kernel, obtaining 96% accuracy with both kernels. In the same manner, in [67], six cameras with a 200 Hz sample rate were used to differentiate between assisted walking of patients with arm support and unassisted walking. The authors also used temporal space features, combining Non-dominated Sorting Genetic Algorithm II (NSGAII) and Genetic Algorithms (GA) with SVM to choose from among 30 marching parameters and conducted the classification, obtaining 99.31% precision. Other authors (e.g., ) combined different signal types, such as EEG, ECG, EOG, EGM and videotaping for sleep evaluation, i.e., distinguishing between the state of waking and sleep of different people. The features used by [PERSON] et al. [68] were Proportional Integration Mode (PIM), Zero Crossings Mode (ZCM) and FFT, to which RBF is applied and then classified with the SVM, obtaining a precision of 88.94%. More techniques utilized by some authors also reduced the number of features to improve the classification accuracy and to increase the processing speed by reducing the dimensionality of the features vector; for instance, in [69], an approach of random forest classification to the diagnosis of lymphatic diseases is proposed. In the first stage, the authors performed a features reduction of different methods, obtaining as best result 92.2% accuracy in the distinction of four states of the patient, including normal, malignant lymph or fibrosis, with the reduction from 18 to 6 features using genetic algorithms. [PERSON] and [PERSON] [70] used ECG signals to differentiate between five kinds of arrhythmias. They used a database offered by MIT-BIH. The procedure was performed in three different stages. The first one consisted in feature extraction by Non-Parametric Power Spectral Density (NPPSD). In the second, the classification using SVM with Gaussian Radial Basis Function was performed. Finally, they accomplished the SVM parameter optimization by a GA. The classification accuracy obtained was 96%. [PERSON] and [PERSON] [42] differentiated arrhythmias using PCA to reduce features in time-frequency space. These features were obtained from ECG by means of the Discrete Orthonormal Stockwell Transform (DOST) concatenated with morphological features. They also employed the PSO technique to adjust the SVM parameters with an RBF kernel. These combined methods, PSO and SVM, reached 98.82% classification accuracy to differentiate among 16 types of arrhythmic events that are produced more frequently in the heart. [PERSON] et al. [71] used the SVM for neuromuscular disorder diagnosis based on the analysis of scalograms formed from MES extracted from the deltoid muscle. Then, the SVM analysis was implemented to subsequently reduce to a single decision parameter. The error probability of this method was 0.5%. Another medical application where an SVM is used for classification is in the differentiation of four main classes in which a protein is composed. In [72], a method is proposed to discriminate between both classes and protein structures by the incorporation of pseudo average chemical shift along with an SVM.