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Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the detection and analysis of epileptic seizures. However, it is often difficult to identify subtle but critical changes in the EEG waveform by visual inspection, thus opening up a vast research area for biomedical engineers to develop and implement several intelligent algorithms for the identification of such subtle changes. Moreover, the EEG signals are nonlinear and non-stationary in nature, which contribute to further complexities related to their manual interpretation and detection of normal and abnormal (interictal and ictal) activities. Hence, it is necessary to develop a Computer Aided Diagnostic (CAD) system to automatically identify the normal and abnormal activities using minimum number of highly discriminating features in classifiers. It has been found that nonlinear features are able to capture the complex physiological phenomena such as abrupt transitions and chaotic behavior in the EEG signals. In this review, we discuss various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail. We also briefly present 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.

Automated EEG analysis of epilepsy: A review

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.

EEG Signal Analysis: A Survey

Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks.

Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks

Decision Support Systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

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EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

The human brain is obviously a complex system, and exhibits rich spatiotemporal dynamics. Among the non-invasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution. Early attempts to analyse EEG data relied on visual inspection of EEG records. Since the introduction of EEG recordings, the volume of data generated from a study involving a single patient has increased exponentially. Therefore, automation based on pattern classification techniques have been applied with considerable success. In this study, a multi-step approach for the classification of EEG signal has been adopted. We have analysed sets of EEG time series recording from healthy volunteers with open eyes and intracranial EEG recordings from patients with epilepsy during ictal (seizure) periods. In the present work, we have employed a discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time - that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. Principal components analysis (PCA) and rough sets have been used to reduce the data dimensionality. A multi-classifier scheme consists of LVQ2.1 neural networks have been developed for the classification task. The experimental results validated the proposed methodology

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Data Mining an EEG Dataset With an Emphasis on Dimensionality Reduction

The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Both error backpropagation and recursive least squares estimation, are applied to the learning scheme. The convergence of training is faster because the least-squares algorithm is applied to the estimation of consequence parameters of the system and backpropagation is applied only to the estimation of the premise parameters. Using the proposed scheme, high-dimensional fuzzy systems can be realized with fewer rules than a typical Takagi-Sugeno fuzzy system. A number of simulations demonstrate the performance of the proposed system.

Classification using adaptive fuzzy inference neural networks

This paper describes the application of rough sets and neural network models for classification of electroencephalogram (EEG) signals from two patient classes: normal and epileptic. First, the wavelet transform (WT) was applied to the EEG time series in order to reduce the dimensionality and highlight important features in the data. Statistical measures of the resulting wavelet coefficients were used for the classification task. Employing rough sets, we sought to determine which of the acquired attributes were necessary/informative as predictors of the decision classes. The results indicate that rough sets was able to accurately classify the datasets with an accuracy of almost 100%. The resulting rule sets were small, with an average cardinality of 6. These results were confirmed using standard neural network based classifiers.

Attribute selection for EEG signal classification using rough sets and neural networks

This paper compares two different rule based classification methods in order to evaluate their relative efficiency with respect to classification accuracy and the caliber of the resulting rules. Specifically, the application of adaptive neuro-fuzzy inference system (ANFIS) and rough sets were deployed on a complete dataset consisting of electroencephalogram (EEG) data. The results indicate that both were able to classify this dataset accurately and the number of rules were similar in both cases, provided the dataset was pre-processed using PCA in the case of ANFIS.

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Pari Jahankhani

Papers

EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Data Mining an EEG Dataset With an Emphasis on Dimensionality Reduction

Classification using adaptive fuzzy inference neural networks

Attribute selection for EEG signal classification using rough sets and neural networks

A rule based approach to classification of EEG datasets: A comparison between ANFIS and rough sets