**5. Results**

Three montages of electrodes are used for clinical experiments as are shown in **Figure 2**. The classification results of for the arrangements are compared with each other to find the best montage. All of the experiments performed with 70% training and 30% testing signals. **Table 1** shows the classification accuracy rates for ANFIS-SVM using these electrode arrangements. Montage III led to best classification and is used as the electrode set for pain classification in this work. The SVM parameters and the related Kernel function are adjusted to achieve the best possible results. This optimization was done using ANFIS as described in the materials and methods section. The over fitting is reduced by controlling the compromise between the training error minimization and the learning capacity of the fuzzy if-then functions. The final decision function parameters can be updated because they depend on the support vectors only.

Furthermore, approximate entropy, Lyapunov exponent and fractal dimension are also examined as non-linear features. An evolutionary feature selection was applied on these elements that showed the theta and alpha ratio and the entropy led to best classification rates for ANFIS-SVM classifier. The accuracies for classification of two classes of pain and no pain are shown in **Table 2** for two cases of using all the features *Pain Identification in Electroencephalography Signal Using Fuzzy Inference System DOI: http://dx.doi.org/10.5772/intechopen.103753*

**Figure 2.**

*The electrode arrangement (a) I, (b) II, and (c) III for electrodes based on 10/20 standard.*


#### **Table 1.**

*ANFIS-SVM Classification rate for three electrode arrangements.*


#### **Table 2.**

*Classification rates of SVM and ANFIS-SVM for reduced and non-reduced features.*

in the feature vector and using the high rank features only. The results for SVM and ANFIS-SVM show the identification rate shows a reduction of 7% and 8% for reduced features, respectively. This reduction happens for identification based on three most effective features, and it could be concluded that the effect of "standard deviation" and "fractal dimension" could not be neglected. The accuracy of 95 % is achieved for ANFIS-SVM proposed method using non-reduced features.

Another evaluation is performed on feature space to find the feature sets for ANFIS-SVM classification. The features are classified as spectral feature set that includes "theta ratio" and "alpha ratio," and nonlinear features namely that include "entropy," "standard deviation" and "fractal dimension." **Table 3** shows the results for classification of pain and no-pain condition. It could be observed that non-linear features result into about 17% improvement for SVM and 14% improvement for ANFIS-SVM classification.


**Table 3.**

*Classification rate for ANFIS-SVM with two sets of features.*
