**4.1 Significance of the analysis**

*Epilepsy - Advances in Diagnosis and Therapy*

linear kernel have achieved almost similar results.

**(seconds)**

**Analysis Training time** 

*Computational complexity of analyses 1 and 2.*

GLCM with ANN

GLCM with SVM

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

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

> **Testing time (seconds)**

105 0.05 100 24

64 0.02 90 10

**Analysis Training time** 

GLCM and statistical features with SVM linear kernel 275 245 GLCM and statistical features with SVM RBF kernel 127 182 GLCM and statistical features with SVM-tuned RBF kernel 220 129

**(seconds)**

**Precision (%)**

> **Testing time (seconds)**

**Miss classification rate (%)**

**202**

**Table 3.**

**Table 2.**

*Computation complexity of analysis 3.* 


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

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


**Table 4.**

*Computational complexity of analysis 6.*

calculations, the analyses prove that the performance of SVM with significant features is good when compared with ANN using large number of features as the input. The major contributions of these analyses in view of the existing work are as follows:



### **Table 5.**

*Summary of EEG analyses.*


**205**

provided the original work is properly cited.

SAAI Centre for research, Thanjavur, Tamilnadu, India

\*Address all correspondence to: bs.nanthini@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

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

the time required and memory space for data parameters are less.

An epileptic seizure is a symptom due to abnormal and excessive irregular neuronal activity in the brain. EEG test is mainly used for diagnosing epilepsy. EEG includes different types of waveforms with different frequency, amplitude, and spatial distribution. Traditional ways of computations would be less efficient for problem-solving. But, soft computing methods can work in an efficient way for discovering solutions from the given data. Components of soft computing are essential for developing automated expert systems. Early diagnosis of disease can save the life of a person. The approved CAD system is able to provide accurate results. Problem-solving is a challenging task for intelligent entities. It has been proved that "a machine can learn new things." It can adapt to new situations and has an ability to learn from the storage information. Supervised learning technique is used in majority of analyses. Fuzzy logic gives multi-value answers, whereas in machine learning, the system learns from data especially with the control or supervisor. In computational intelligence, evolutionary algorithms are inspired by biological systems and give optimal solution for the problem. "Clean data are greater than more data." Machine learns from data. Quality of data is important rather than quantity of data. This chapter gave an introduction about the components of soft computing and classification in machine learning. From the review of analyses, this chapter concludes that relevant features and less number of features can make the classifier perform well. Accuracies are compared in all decomposed signals and proved that level 4 of decomposition is enough for EEG signal classification. At level 4, the lower frequencies (delta and theta) can be analyzed perfectly because seizures occur mostly at lower frequencies. Also, from the analyses, it has been proved that

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

**5. Conclusion**

**Conflict of interest**

**Author details**

B. Suguna Nanthini

There are no conflicts of interest.
