**1. Introduction**

The human brain contains billions of neurons which vibrate and generate oscillatory activity. This neural activity of nervous system is studied through brainwaves. These waves are highly complex and can be recorded using the method called electroencephalography (EEG). An epileptic seizure is a symptom due to abnormal and irregular excessive neuronal activity in the brain. The neuronal activity can be recorded by medical tests. Various methods are available for diagnosing brain diseases. Among those methods, the electroencephalogram test is mainly used for diagnosing epilepsy. EEG includes different types of waveforms with different frequency, amplitude, and spatial distribution. The electrical activity of the brain differs due to different stimuli and physiological variables. An EEG test can provide detailed information about the electrical activity of the brain at the testing time. The neurologist recognizes the brain pattern from the EEG test results to diagnose epilepsy. EEG recordings by visual scanning will take time and are inaccurate for detecting epilepsy [1]. Nowadays, the technology of computer-aided

diagnosis (CAD) has been used in hospitals; it cannot replace the doctor, but it can assist the professionals to diagnose the disease accurately. The main aim of the CAD systems is to identify the disease in early stages of its development. The CAD supportive tool is developed by using highly complex recognition techniques and machine learning algorithms. The CAD systems are approved by US Food and Drug Administration. They can reduce the false negative rate of recognition of diseases. Recent research studies have identified that the performance of CAD is better in the clinical environment. Establishing CAD systems in medical practice contains some risk and complexity. Sometimes, the interpretation of given data may not yield 100% accurate result. It provides only secondary opinion to the physicians. Especially in epileptic seizure detection, machine learning is very difficult because of understanding the brainwaves. The patterns of brainwaves are completely unique to individuals. Since 1998, CAD tools have been useful for diagnosing disease. It does not mean that they are meant for diagnostic purposes, but the approved CAD system can provide accurate results. Early diagnosis of disease is very important for saving life. Different information can be extracted by using medical image and signal technologies such as X-ray, computed tomography (CT), positron emission tomography (PET), single positron emission computed tomography (SPECT), magnetic resonance imaging (MRI), ultrasound, EEG, electrocardiography (ECG), electromyography (EMG), etc. for diagnosing diseases like cancer and coronary artery, cardiovascular, and neurological disorders. CAD supports accurate diagnosis in early stages of a chronic disease. Soft computing techniques are used in computer-aided diagnosis and computer-aided detection. In the earlier stage of computational approaches, the problem-solving methods were carried out using conventional mathematics and specific analytical models [2].

The traditional way of computing would be less efficient for problem-solving. In the growth of computational science, researchers focus on soft computing in order to overcome the drawbacks of hard computing. Just like artificial


**195**

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

intelligence (AI), soft computing method works similar to the human brain. A comparison of hard computing and soft computing is given in **Table 1**. Soft computing techniques can be applicable in various fields such as signal and image processing, system integration, decision support process and system control, pattern recognition, fault diagnosis, data mining, forecasting applications, robotics, virtual reality, etc. Machine learning, fuzzy logic, evolutionary computation, Bayesian network, and chaos theory techniques are the main components of soft computing. These methods are very useful for automation and necessary for technology development [2]. The following sections of this chapter explain the merits and demerits of soft computing and the procedure for automated epileptic seizure

Machine learning, fuzzy logic, evolutionary computation, and probabilistic ideas are the main components of soft computing. The following sections give

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. Machine learning techniques include artificial neural networks (ANNs), perceptron, and support vector machine (SVM) whereas evolutionary computations include evolutionary algorithms, meta-heuristic and swam intelligence. Just like human brain, a machine is capable of acquiring knowledge from data. It is developed from the field of AI. In order to build intelligent machines, we need machine learning techniques. These techniques deal with huge data in minimum time. There are different types of machine learn-

Supervised learning technique is used in majority of analyses. In this technique,

the system learns from training examples, whereas in unsupervised learning, the system is challenged to discover some patterns directly from the given data. Classification and regression are two different supervised learning problems. The next section gives detailed description about classification using EEG signals for epileptic seizure detection. Regression gives the statistical relationship between two or more variables. An association rule learning problem and clustering problem are major examples explaining unsupervised learning problems. Association rule learning is based on rule-based machine learning method and used to discover the interesting relationship between variables in a huge database whereas clustering method discovers the patterns from the groupings of given data. Reinforcement learning is the third type of machine learning which learns how to behave in an environment merely by interaction. It is a dynamic way of learning. It learns directly and controls the data (no supervisor). Machine learning algorithms have the ability of learning from data and make predictions and classifications for a model based on the sample

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

prediction and detection.

**2.1 Machine learning**

**2. Components of soft computing**

detailed descriptions of each component.

ing methods. They are as follows:

• unsupervised learning; and

• reinforcement learning.

• supervised learning;

*Components of Soft Computing for Epileptic Seizure Prediction and Detection DOI: http://dx.doi.org/10.5772/intechopen.83413*

intelligence (AI), soft computing method works similar to the human brain. A comparison of hard computing and soft computing is given in **Table 1**. Soft computing techniques can be applicable in various fields such as signal and image processing, system integration, decision support process and system control, pattern recognition, fault diagnosis, data mining, forecasting applications, robotics, virtual reality, etc. Machine learning, fuzzy logic, evolutionary computation, Bayesian network, and chaos theory techniques are the main components of soft computing. These methods are very useful for automation and necessary for technology development [2]. The following sections of this chapter explain the merits and demerits of soft computing and the procedure for automated epileptic seizure prediction and detection.
