Epileptic Seizure Classification Using Neural Networks with 14 Features
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Epileptic seizure detection in EEGs using time-frequency analysis
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Entropies based detection of epileptic seizures with artificial neural network classifiers
Expert Systems with Applications: An International Journal
Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
EURASIP Journal on Advances in Signal Processing
EEG based automated detection of auditory loss: A pilot study
Expert Systems with Applications: An International Journal
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines
Expert Systems with Applications: An International Journal
Possibilistic entropy: a new method for nonlinear dynamical analysis of biosignals
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A fuzzy logic system for seizure onset detection in intracranial EEG
Computational Intelligence and Neuroscience
Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis
Journal of Medical Systems
Artificial Intelligence in Medicine
Possibilistic nonlinear dynamical analysis for pattern recognition
Pattern Recognition
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
An algorithm for on-line detection of high frequency oscillations related to epilepsy
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
A hybrid intelligent system for medical data classification
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the 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 proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system. Two different types of neural networks, namely, Elman and probabilistic neural networks, are considered in this paper. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system