Journal ArticleDOI
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features
TLDR
Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.Abstract:
Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.read more
Citations
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Journal ArticleDOI
Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis
TL;DR: The suitability of the time-frequency ( t-f) analysis to classify EEG segments for epileptic seizures, and several methods for t- f analysis of EEGs are compared.
Journal ArticleDOI
Automated EEG analysis of epilepsy: A review
TL;DR: This review discusses various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail, and briefly presents the various open ended challenges that need to be addressed before a CAD based epilepsy detection system can be set-up in a clinical setting.
Journal ArticleDOI
Short Communication: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model
TL;DR: A multilayer perceptron neural network (MLPNN) based classification model as a diagnostic decision support mechanism in the epilepsy treatment showed that the proposed model resulted in satisfactory classification accuracy rates.
Journal ArticleDOI
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks
TL;DR: ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks and it is shown that the overall accuracy values as high as 100% can be achieved by using the proposed systems.
Journal ArticleDOI
Automated diagnosis of epileptic EEG using entropies
U. Rajendra Acharya,Filippo Molinari,S. Vinitha Sree,Subhagata Chattopadhyay,Kwan Hoong Ng,Jasjit S. Suri +5 more
TL;DR: This work proposes a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals and shows that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%.
References
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