Essam H. Houssein

Feature extraction of epilepsy EEG using discrete wavelet transform

Epilepsy is one of the most common a chronic neurological disorders of the brain that affect millions of the worlds populations. It is characterized by recurrent seizures, which are physical reactions to sudden, usually brief, excessive electrical discharges in a group of brain cells. Hence, seizure identification has great importance in clinical therapy of epileptic patients. Electroencephalogram (EEG) is most commonly used in epilepsy detection since it includes precious physiological information of the brain. However, it could be a challenge to detect the subtle but critical changes included in EEG signals. Feature extraction of EEG signals is core trouble on EEG-based brain mapping analysis. This paper will extract ten features from EEG signal based on discrete wavelet transform (DWT) for epilepsy detection. These numerous features will help the classifiers to achieve a good accuracy when utilize to classify EEG signal to detect epilepsy. Subsequently, the results have illustrated that DWT has been adopted to extract various features i.e., Entropy, Min, Max, Mean, Median, Standard deviation, Variance, Skewness, Energy and Relative Wave Energy (RWE).

MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

Grasshopper Optimization Algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective Grasshopper Optimization Algorithm (MOGOA). An external archive is integrated with the GOA for saving the Pareto optimal solutions. The archive is then employed for defining the social behavior of the GOA in the multi-objective search space. To evaluate and verify the effectiveness of the MOGOA, a set of standard unconstrained and constrained test functions are used. Moreover, the proposed algorithm was compared with three well-known optimization algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Ant Lion Optimizer (MOALO), and Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II); and the obtained results show that the MOGOA algorithm is able to provide competitive results and outperform other algorithms.

A Hybrid EEG Signals Classification Approach Based on Grey Wolf Optimizer Enhanced SVMs for Epileptic Detection

This paper proposes a hybrid Electroencephalogram (EEG) classification approach based on grey wolf optimizer (GWO) enhanced support vector machines (SVMs) called GWO-SVM approach for automatic seizure detection. In order to decompose EEG into five sub-band components, the discrete wavelet transform (DWT) was utilized to extracted features set. Then, this features are used to train the SVM with radial basis function (RBF) kernel function. Further, GWO was used for selecting the significant feature subset and the optimal parameters of SVM in order to obtain a successful EEG classification. The experimental results proved that the proposed GWO-SVM approach, able to detect epileptic and could thus further enhance the diagnosis of epilepsy with accuracy 100%. Furthermore, the proposed approach has been compared with genetic algorithm (GA) with support vector machines (GA-SVMs) and SVM using RBF kernel function. The computational results reveal that GWO-SVM approach achieved better classification accuracy outperforms both GA-SVM and typical SVMs.

Improved grasshopper optimization algorithm using opposition-based learning

Abstract This paper proposes an improved version of the grasshopper optimization algorithm (GOA) based on the opposition-based learning (OBL) strategy called OBLGOA for solving benchmark optimization functions and engineering problems. The proposed OBLGOA algorithm consists of two stages: the first stage generates an initial population and its opposite using the OBL strategy; and the second stage uses the OBL as an additional phase to update the GOA population in each iteration. However, the OBL is applied to only half of the solutions to reduce the time complexity. To investigate the performance of the proposed OBLGOA, six sets of experiment series are performed, and they include twenty-three benchmark functions and four engineering problems. The experiments revealed that the results of the proposed algorithm were superior to those of ten well-known algorithms in this domain. Eventually, the obtained results proved that the OBLGOA algorithm can provide competitive results for optimization engineering problems compared with state-of-the-art algorithms.

EEG signals classification for epileptic detection: a review

Electroencephalogram (EEG) is prevalently applied in the detection and prediction of epileptic seizures, and is used to measure and record the electrical brain activity. However, it is often difficult to distinguish subtle but critical changes in the EEG waveform by human eye inspection (visual inspection), thus open a new research field for biomedical engineers to develop and implement several intelligent techniques for the identification of such subtle changes. The EEG signals categorized as non-linear and non-stationary in nature, which achieve to further complexities according to their manual interpretation and detection of normal and abnormal activities. Several classifiers have been proposed to improve accurate and computationally efficient techniques for EEG signal. Swarm optimization algorithms and machine learning classification techniques are usually used to obtain such analysis. This paper introduces a review for some of the preprocessing, feature extraction and classification methodologies used in EEG signals.

ECG signals classification: a review

Electrocardiogram (ECG), non-stationary signals, is extensively used to evaluate the rate and tuning of heartbeats. The main purpose of this paper is to provide an overview of utilizing machine learning and swarm optimization algorithms in ECG classification. Furthermore, feature extraction is the main stage in ECG classification to find a set of relevant features that can attain the best accuracy. Swarm optimization algorithm is combined with classifiers for the purpose of searching the best value of classification parameters that best fits its discriminant purpose. Finally, this paper introduces an ECG heartbeat classification approach based on the water wave optimization (WWO) and support vector machine (SVM). Published literature presented in this paper indicates the potential of ANN and SVM as a useful tool for ECG classification. Author strongly believes that this review will be quite useful to the researchers, scientific engineers working in this area to find out the relevant references.

Maximizing Lifetime of Wireless Sensor Networks Based on Whale Optimization Algorithm

The lifetime of wireless sensor networks (WSNs) are considered one of the most challenges that face the topology control of WSNs. Topology control of WSNs is a technique to optimize the connections between nodes to reduce the interference between them, save energy and extend network lifetime. In this paper proposed an algorithm based on Whale Optimization Algorithm (WOA) called WOTC, the paper provides a discrete version of the WOA, where the position of each Whale is calculate and represented in a binary format. The proposed fitness function is designed to consider two main target; a minimization in numbers of active nodes, and low energy consumption within these nodes to overcome challenges that face topology control to prolong the WSNs lifetime, the simulations were carried out using Attaraya a simulator. Consequently, the results showed that the final topology obtained by WOTC is better than A3 topology depending on the number of neighbors and their energies for active nodes, use a graph traversal function to ensure that all nodes which selected in network are covered in the best topology selection.

A Two-Stage Feature Extraction Approach for ECG Signals

This paper investigate various techniques of extracting features from the electrocardiogram (ECG) signal in order to analyze the ECG signals to detect the heart disease. Feature extraction, is a one of the widespread process of decompose the ECG data. This paper introduce a two-stage feature extraction approach to extract features from ECG signals for different types of arrhythmias. Firstly, Modified Pan-Tomkins Algorithm (MPTA) is implemented to remove noise and extract nine features. Then the proposed Improved Feature Extraction Algorithm (IFEA) is applied to extract additionally ten different features from the ECG signal. The MIT-BIH arrhythmia database have been used to test the proposed approach. It is obvious from the results that the proposed approach shows a high classification in terms of the following four statistical measures: Accuracy (Ac) 98.37%, Recall 48.29%, Precision 43.91%, F Measure 45.31%, and Specificity (Sp) 93.30%, respectively.

S-shaped Binary Whale Optimization Algorithm for Feature Selection

Whale optimization algorithm is one of the recent nature-inspired optimization technique based on the behavior of bubble-net hunting strategy. In this paper, a novel binary version of whale optimization algorithm (bWOA) is proposed to select the optimal feature subset for dimensionality reduction and classifications problem. The new approach is based on a sigmoid transfer function (S-shape). By dealing with the feature selection problem, a free position of the whale must be transformed to their corresponding binary solutions. This transformation is performed by applying an S-shaped transfer function in every dimension that defines the probability of transforming the position vectors’ elements from 0 to 1 and vice versa and hence force the search agents to move in a binary space. K-NN classifier is applied to ensure that the selected features are the relevant ones. A set of criteria are used to evaluate and compare the proposed bWOA-S with the native one over eleven different datasets. The results proved that the new algorithm has a significant performance in finding the optimal feature.

Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images

There is a remarkable increase in the popularity of swarms inspired algorithms in the last decade. It offers a kind of flexibility and efficiency in their applications in different fields. These algorithms are inspired by the behaviour of various swarms as birds, fish and animals. This chapter presents an overview of some algorithms as grey wolf optimization (GWO), artificial bee colony (ABC) and antlion optimization (ALO). It proposed swarm optimization approaches for liver segmentation based on these algorithms in CT and MRI images. The experimental results of these algorithms show that they are powerful and can get remarkable results when applied to segment liver medical images. It is evidently proved from the experimental results that ALO, GWO and ABC have obtained 94.49%, 94.08% and 93.73%, respectively, in terms of overall accuracy using similarity index measure.