**4. Review of EEG signal analyses**

B. Suguna Nanthini [3] had carried out six different analyses for detecting seizures using EEG signals under supervised learning method. The performance of the system in all the analyses is measured by the confusion matrix method. Online available EEG database (Bonn University Database) and real-time data from the EEG center, Coimbatore, India, are used for EEG signal classification analysis. EEG tests taken from 10 normal and seizure subjects for epileptic seizure detection are

used in second database. These signals are examined and used for binary classification as well as for validation. Set A (perfectly normal) and Set E (merely seizure) have been chosen from online database. The first three analyses were carried out in the spatial domain and next three analyses were carried out in the frequency (wavelet) domain. In the first analysis [9], gray-level co-occurrence matrix (GLCM) features namely contrast, correlation, energy, and homogeneity are extracted from the EEG vectors. The system is well trained to identify the exact group and tested for classification of data using ANN classifier. The performance of the system is measured by the confusion matrix. The system achieves 85% accuracy. The same problem is examined with an SVM classifier in the second analysis [10]. The classifier achieves 90% accuracy for EEG signal classification. The computational complexity of analyses 1 and 2 are calculated and shown in the following **Table 2**.

When the analyses use ANN and SVM classifiers, the space complexity depends on the number of training samples used in the classification process. In the third analysis [11], eight statistical features are added with GLCM features. The EEG signals are segmented and combinations of normal and seizure signals are used for classification process. In extraction process, eight statistical features and four GLCM features are extracted from each of the segmented signal. An SVM classifier with different kernels is used for seizure detection. The computation complexity of analysis 3 is calculated and presented in the following **Table 3**. The complexity of the model depends on k-fold cross-validation method. The system executes the same learning algorithm k times. It takes different training sets of size (k−1)/k times the size of the original data. In the execution step, each sample is evaluated (k−1) times. The space complexity of the analysis for RBF kernel is (Number of samples) ^2\*(Number of features) and for linear kernel is (Number of samples) \* (Number of features). ANN with back propagation algorithm [9] and SVM with linear kernel have achieved almost similar results.

EEG signals are non-stationery and can be analyzed better through wavelet transform. Different types of wavelets are available to decompose the signal. The challenging part is to select a suitable wavelet and the level of decomposition of the signal. In the fourth analysis [12], statistical features namely mean, median, mode, standard deviation, skewness and kurtosis and four GLCM features are extracted


### **Table 2.**

*Computational complexity of analyses 1 and 2.*


**203**

**Table 4.**

*Computational complexity of analysis 6.*

*Components of Soft Computing for Epileptic Seizure Prediction and Detection*

from the EEG signal. The performance of the system is measured to select a suitable classifier for seizure detection. ANN and SVM are two classifiers used in the fourth analysis. The wavelets namely db1, db2, and haar are used for signal decomposition.

1.Statistical and GLCM features are used to examine the EEG signals separately

2.Raw EEG data (0–60 Hz) as its subbands (30–60 Hz (cD1), 15–30 Hz (cD2), 8–15 Hz (cD3), and 0–8 Hz (cA3)) are verified and analyzed using all those

and further they are combined together as an input to the classifier.

3.On comparison of features (statistical, GLCM, and their combination), wavelets (db1,db2, and haar), and classifiers (ANN and SVM), the analysis concluded that the combination of statistical and GLCM features using SVM

To extract maximum information from the EEG signal, entropy features are used in the fifth analysis [13]. There are different types of entropies. In this analysis, Shannon, Renyi, and Tsallis entropies are extracted from the EEG signals. On comparison of entropy features, the analysis concluded that Renyi entropy can achieve successful result. Instead of using only statistical features over the wavelet coefficient, this analysis examines the EEG signals through entropy values obtained from different degrees of orders for classification. When comparing with the existing work, this research uses the extended version of Shannon, namely Renyi and Tsallis to extract the maximum information from each EEG signal vector in terms of probability events. In the sixth analysis [14]**,** EEG signals are examined by combining all the features from the previous analysis. Altogether, 16 features from the methods namely GLCM, statistical, and Renyi entropy features are extracted from the raw EEG and its subbands. DWT (db2) is used for decomposition of the signal at level 4.The approximation and detail co-efficient are analyzed individually with 16 and 8 features, respectively. Genetic algorithm is used for selecting 8 appropriate features. The SVM is used as a classifier. Classification is carried out for seizure detection. Accuracies from 16 and 8 dimension features are compared and it is concluded that relevant features can give better accuracy. Moreover, level 4 is enough for decomposing the signal because the lower frequencies namely delta and theta can be obtained at level 4 of decomposition. Mostly, seizures are identified at lower frequencies; so, level 4 is sufficient for decomposition of the EEG signal. Further, the time to execute the algorithm is reduced and it occupies less memory space for the storage of data parameters. The complexity of this EEG signal analysis

Summary and time complexity of the analyses are shown in **Tables 5** and **6**, respectively. All analyses are carried out in MATLAB environment. From the

**Analysis Training time (seconds) Testing time (seconds)** 16 Dimension features with SVM 0.76 l 0.0013 8 Dimension features with SVM classifier 0.72 0.0011

*DOI: http://dx.doi.org/10.5772/intechopen.83413*

The signal is decomposed up to level 3.

classifier gives the best outcome.

is calculated and presented in the following **Table 4**.

**4.1 Significance of the analysis**

features.

### **Table 3.**

*Computation complexity of analysis 3.* 

from the EEG signal. The performance of the system is measured to select a suitable classifier for seizure detection. ANN and SVM are two classifiers used in the fourth analysis. The wavelets namely db1, db2, and haar are used for signal decomposition. The signal is decomposed up to level 3.
