Swarm Negative Selection Algorithm for Electroencephalogram Signals Classification

Abstract

Problem statement:The process of epilepsy diagnosis from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. Therefore, there is a strong need to automate this process. Such automated systems must rely on robust and effective algorithms for detection and prediction. Approach: The proposed detection system of epileptic seizure in EEG signals is based on Discrete Wavelet Transform (DWT) and Swarm Negative Selection (SNS) algorithm. DWT was used to analyze EEG signals at different frequency bands and statistics over the set of the wavelet coefficients were calculated to introduce the feature vector for SNS classifier. The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. Thus, adapted negative selection is employed to create a set of self-tolerant ALCs. Whereas, PSO is used to evolve these ALCs away from self patterns towards non-self space and to maintain diversity and generality among the ALCs. Results: The experimental results proved that the proposed method reveals very promising performance in classifying EEG signals. A comparison with many previous studies showed that the presented algorithm has better results outperforming those reported by earlier methods. Conclusion: The technique was approved to be robust and effective in detecting and localizing epileptic seizure in EEG recording. Hence, the proposed system can be very helpful to make faster and accurate diagnosis decision.

An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development

“…Minimal side effects include slight redness/skin irritation at the contact site of the electrode, but this typically wears off in a few hours [12]. As a result, EEGs have been widely used as a clinical tool to measure brain activity [13]. In this paper, we have not only devised methods that outperform many existing techniques in epileptic seizure detection from EEG signal analysis but also implemented the ideas as a mobile development application and a functional framework.…”

“…Moreover, epilepsy diagnosis by EEG signals requires a human evaluator and is laborious and expensive, as it requires long hours of expert analysis, which means it can be prone to human error. Therefore, many studies have focused on developing an automated, computerized model for the analysis of EEG signals [1,13].…”

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

“…The IPSO algorithm can prevent premature convergence and outperform the other existing methods. Nasser Omer Sahel Ba-Karait et al [161] proposed detection system of epileptic seizure in EEG signals which is based on Discrete Wavelet Transform (DWT) and Swarm Negative Selection (SNS) algorithm. DWT was used to analyze EEG signals at different frequency bands and statistics over the set of the wavelet coefficients were calculated to introduce the feature vector for SNS classifier.…”

Comparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram Signal

“…• Deriving fuzzy rules from trained neural networks • Fuzzy logic based tuning of neural network training parameter • Fuzzy logic criteria for increasing a network size • Realizing fuzzy membership function through clustering algorithm in unsupervised learning in SOMs and neural network • Representing Fuzzification, fuzzy inference and defuzzification through multilayer feed-forward connectionist networks (Karait et al, 2009) Structure of NEURO-fuzzy system: Two possible models of neuro-fuzzy systems are:…”