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

Artificial neural networks are finding many uses in the medical diagnosis application The goal of this paper is to evaluate artificial neural network in disease diagnosis Two cases are studied The first one is acute nephritis disease; data is the disease symptoms The second is the heart disease; data is on cardiac Single Proton Emission Computed Tomography (SPECT) images Each patient classified into two categories: infected and non-infected Classification is an important tool in medical diagnosis decision support Feed-forward back propagation neural network is used as a classifier to distinguish between infected or non-infected person in both cases The results of applying the artificial neural networks methodology to acute nephritis diagnosis based upon selected symptoms show abilities of the network to learn the patterns corresponding to symptoms of the person In this study, the data were obtained from UCI machine learning repository in order to diagnosed diseases The data is separated into inputs and targets The targets for the neural network will be identified with 1's as infected and will be identified with 0's as non-infected In the diagnosis of acute nephritis disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 99 percent while in the diagnosis of heart disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 95 percent

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Artificial Neural Networks in Medical Diagnosis

We propose new features for classification of epileptic seizure EEG signals.Features were extracted from PSR of IMFs of EEG signals.We define ellipse area of 2D PSR and IQR of Euclidian distance of 3D PSR as features.LS-SVM classifier has been used for classification with the proposed features.Results were compared with other existing methods studied on the same EEG dataset. Epileptic seizure is the most common disorder of human brain, which is generally detected from electroencephalogram (EEG) signals. In this paper, we have proposed the new features based on the phase space representation (PSR) for classification of epileptic seizure and seizure-free EEG signals. The EEG signals are firstly decomposed using empirical mode decomposition (EMD) and phase space has been reconstructed for obtained intrinsic mode functions (IMFs). For the purpose of classification of epileptic seizure and seizure-free EEG signals, two-dimensional (2D) and three-dimensional (3D) PSRs have been used. New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals. Two measures have been defined namely, 95% confidence ellipse area for 2D PSR and interquartile range (IQR) of the Euclidian distances for 3D PSR of IMFs of EEG signals. These measured parameters show significant difference between epileptic seizure and seizure-free EEG signals. The combination of these measured parameters for different IMFs has been utilized to form the feature set for classification of epileptic seizure EEG signals. Least squares support vector machine (LS-SVM) has been employed for classification of epileptic seizure and seizure-free EEG signals, and its classification performance has been evaluated using different kernels namely, radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernels. Simulation results with various performance parameters of classifier, have been included to show the effectiveness of the proposed method for classification of epileptic seizure and seizure-free EEG signals.

Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions

http://karimjerbi.com/PDF/Lajnef_etal_JNeurosciMethods_2015.pdf

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

In this study, a method is proposed to detect epileptic seizures over EEG signal. For this purpose, a linear prediction filter is used to observe the presence of spikes and sharp waves on seizure EEG recordings. Linear prediction analysis calculates a coefficient set for each window, which can best model the applied time series signal. Modeling success is observed on the prediction error signal. The presence of spikes and other seizure-specific sharp waves on the signal reduces the modeling success and increases the prediction error of the filter. It is clearly observed that, the energy of prediction error signal during seizures is much higher than that of the seizure free intervals, which indicates the energy value and can be used to locate the seizure interval. The method is applied to 250 distinct EEG records, each of which has 23.6s duration. The results of the proposed algorithm are evaluated with the ROC analysis which indicates 93.6% success in detecting the presence of seizures. As a conclusion, the linear prediction error energy method can be considered as an efficient way to detect epileptic seizures on EEG records.

Epileptic EEG detection using the linear prediction error energy

Uroflowmetry is a measuring method, which provides numerical and graphical information about patient's lower urinary tract dynamics by measuring and plotting the rate of change in urine volume. The main purpose of this study is to analyze the uroflowmetric data and to assist physicians for their diagnosis. An expert pre-diagnosis system is implemented for automatically evaluating possible symptoms from the uroflow signals. The system uses artificial neural networks (ANN) and produces a pre-diagnostic result. The outputs of ANN are classified into three groups, which are, ''healthy'', ''possible pathologic'' and ''pathologic''. The ANN is trained using back-propagation method and the inputs of the ANN are the extracted features, which are selected according to the suggestions of urology specialists. The proposed system is trained and validated using a dataset of patients, who have already diagnosed by the specialists.

A new approach to urinary system dynamics problems: Evaluation and classification of uroflowmeter signals using artificial neural networks

Uroflowmetry is a measuring method, which provides numerical and graphical information about patient's lower urinary tract dynamics by measuring and plotting the rate of change of voided urine volume. The main purpose of he study is to evaulate uroflowmetric data using artificial neural networks (ANN) and provide a pre-diagnostic result for urology specialists. The ANN is trained using back-propagation method and the inputs of ANN are the results of a special feature extraction algorithm, which is designed with the suggestions of urology specialists. System's success is monitored with a set of data, which was already diagnosed by specialists. The outputs of ANN are classified into three groups, namely, "healthy", "possible pathologic" and "pathologic".

Semih Altunay

Papers

Epileptic EEG detection using the linear prediction error energy

A new approach to urinary system dynamics problems: Evaluation and classification of uroflowmeter signals using artificial neural networks

Interpretation of Uroflow Graphs with Artificial Neural Networks