Interictal EEG Signals Research Articles

Epileptic electroencephalogram recognition based on discrete S transform and permutation entropy

Electroencephalogram(EEG) analysis has important reference value in the diagnosis of epilepsy. The automatic classification of epileptic EEG can be used to judge the patient's situation in time,which is of great significance in clinical application. In order to solve the problem that the recognition accuracy is not high by using the single feature of EEG signals and avoid the influence of wavelet basis function selection on recognition results,a method of automatic discrimination of epileptic EEG signals based on S transform and permutation entropy is proposed. Firstly, the original signals are decomposed by discrete S transform, and then we calculate the fluctuation index of coefficients of each rhythm and combine the permutation entropy of EEG signals into a feature vector and use Real AdaBoost classifier to discriminate the epileptic EEG signals in muti-period. In this study, we used the epilepsy database from University of Bonn. Three groups of EEG signals, including the data of normal people with their eyes open, the data collected inside of the epileptic foci from patients during their interictal period and the data during their ictal period, were used to test effectiveness. The results of this study showed that the fluctuation index of each rhythm could be used to characterize the normal, interictal and ictal epileptic EEG signals effectively, and the recognition accuracy of multiple features was much higher than that of single feature. The average recognition accuracy could reach 98.13%. Compared with time-frequency feature extraction method or nonlinear feature extraction method only,the recognition accuracy was increased by more than 1.2% and 8.1% respectively, which was superior to the methods recorded in many other literatures. Therefore, this method has a good application prospect in diagnosis of epilepsy.

Classification of Seizure Prone EEG Signal Using Amplitude and Frequency Based Parameters of Intrinsic Mode Functions

Epilepsy is a serious neurological disorder which disturbs the common activities of a human brain. It is detected with the help of electroencephalogram (EEG) recording of the brain as it covers detailed information related to the different physiological states of the brain. A novel method based on empirical mode decomposition (EMD) with amplitude and frequency based parameters is presented in this paper to classify the seizure activity from an EEG signal. A well-known dataset consisting of EEG signals of healthy volunteers and epileptic patients, under different stages, has been used in this study. EMD has been employed for the decomposition of an EEG signal into a set of intrinsic mode functions (IMFs). First four IMFs have been considered in this work. Two amplitude based parameters and one frequency based parameter have been extracted from each of the four IMFs. The validation of the extracted parameters of all IMFs has been carried out by Kruskal–Wallis statistical test and box plot method. Extracted parameters qualify both the validation tests for discriminating normal, interictal and ictal EEG signals. Twelve features from each EEG signal have been fed to four different classifiers; artificial neural network (ANN), least square-support vector machine (LS-SVM), random forest and Naive Bayes. The performance of the study has been evaluated in two stages. In the first stage, parameters from first four IMFs have been analyzed individually, with four classifiers and in the second stage, the performance has been analyzed by considering the parameters of all the four IMFs. The results of the proposed method have been compared with the other state of the art techniques for the validation.

Seizure Prediction Using Undulated Global and Local Features

In this study, a seizure prediction method is proposed based on a patient-specific approach by extracting undulated global and local features of preictal/ictal and interictal periods of EEG signals. The proposed method consists of feature extraction, classification, and regularization. The undulated global feature is extracted using phase correlation between two consecutive epochs of EEG signals and an undulated local feature is extracted using the fluctuation and deviation of EEG signals within the epoch. These features are further used for classification of preictal/ictal and interictal EEG signals. A regularization technique is applied on the classified outputs for the reduction of false alarms and improvement of the overall prediction accuracy (PA). The experimental results confirm that the proposed method provides high PA (i.e., 95.4%) with low false positive per hour using intracranial EEG signals in different brain locations of 21 patients from a benchmark dataset. Combining global and local features enables the transition point to be determined between different types of signals with greater accuracy, resulting successful versus unsuccessful prediction of seizure. The theoretical contribution of this study may provide an opportunity for the development of a clinical device to predict forthcoming seizure in real time.

Automatic seizure detection of electroencephalogram signals based on frequency slice wavelet transform and SVM

Over 50 million people all over the world are suffering from epilepsy It is of great significance to achieve automatic seizure detection in electroencephalogram (EEG) signal for clinical diagnosis and treatment. In order to achieve automatic diagnosis of epilepsy, a multitude of automated computer aided diagnostic techniques have been proposed. However, only a few of studies lay emphasis on the effects of different rhythm signals. To explore the influence of rhythm signals on classification accuracy, a newly-developed time-frequency analysis method called frequency slice wavelet transform (FSWT), which is able to locate arbitrary time-frequency range with the use of frequency slice function and whose inverse transformation only relies on fast Fourier transform, is employed to extract five different rhythm signals, namely (0.5-4 Hz), (4-8 Hz), (8-13 Hz), (13-30 Hz) and (30-50 Hz) from original EEG signal. Subsequently, for extracting the nonlinear and linear features, the approximate entropy of each rhythm signal and fluctuation index of adjacent rhythm signals are calculated to reflect the variation characteristics of rhythm signals and they are flocked together to form the nine-dimensional feature vectors. Finally, the extracted vectors are fed into a support vector machine (SVM) which is optimized by genetic algorithms (GA) for classification. Specifically, since the parameters of SVM are associated with the final classification accuracy and appropriate parameters could lead to a remarkable result, GA is applied to parameter optimization, half of the obtained vectors are randomly selected as a training set for training, and the remaining vectors constitute a testing set to test the established model. Experimental results of the proposed approach, which is employed in a public epileptic EEG dataset obtained from department of epitology at Bonn University for validation indicate that the proposed method in this study can carry out the task of classifying normal, inter-ictal and epileptic seizure EEG signals with a high classification accuracy (98.33%), a sensitivity of 99%, a specificity of 99%, and a positive predictive value of 99.5%. The presented approach provides an outstanding scheme for the automatic diagnosis of epilepsy, and the directions of our further research may include the application of the proposed method to the diagnosis of other disorders.

Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals

Epilepsy is a neurological disorder affecting more than 50 million individuals in the world. Analysis of the electroencephalogram (EEG) is a powerful tool to assist neurologists for diagnosis and treatment. In this paper a new feature extraction method based on empirical mode decomposition (EMD) is proposed. The EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and four statistical parameters are calculated over these IMFs constituting the input feature vector to be fed to a multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the publicly available Bonn dataset show that an accurate classification rate of 100% is achieved in the discrimination between normal and ictal EEG, and an accuracy of 97.7% is reached in the classification of interictal and ictal EEG signals. Our results are equivalent or outperform recent studies published in the literature.

Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals

Electroencephalogram (EEG) has a great potential for diagnosis and treatment of brain disorders like epileptic seizure. Feature extraction and classification of EEG signals is the crucial task to detect the stages of ictal and interictal signals for treatment and precaution of epileptic patients. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering the non-abruptness phenomena and inconsistency in different brain locations. In this study, the authors present a new approach for feature extraction and classification by exploiting temporal correlation within EEG signals for better seizure detection as any abruptness in the temporal correlation within a signal represents the transition of a phenomenon. In the proposed methods, they divide an EEG signal into a number of epochs and arrange them into two-dimensional matrix and then apply different transformation/decomposition to extract a number of statistical features. These features are then used as an input into LS-SVM to classify them. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity and accuracy of ictal and interictal period of epilepsy for benchmark datasets and different brain locations.

Seizure prediction by analyzing EEG signal based on phase correlation.

Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals Using Phase Correlation.

Automated seizure prediction has a potential in epilepsy monitoring, diagnosis, and rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and prediction. This paper proposes a new seizure prediction approach based on spatiotemporal relationship of EEG signals using phase correlation. This measures the relative change between current and reference vectors of EEG signals which can be used to identify preictal/ictal (before the actual seizure onset/ actual seizure period) and interictal (period between adjacent seizures) EEG signals to predict the seizure. The experiments show that the proposed method is less sensitive to artifacts and provides higher prediction accuracy (i.e., 91.95%) and lower number of false alarms compared to the state-of-the-art methods using intracranial EEG signals in different brain locations of 21 patients from a benchmark data set.

NOVEL APPROACHES OF EEG SIGNAL CLASSIFICATION USING IMF BANDWIDTH AND DCT FREQUENCY

Electroencephalogram (EEG) is a record of ongoing electrical signal to represent the human brain activity. It has great potential for the diagnosis to treatment of mental disorder and brain diseases such as epileptic seizure. Features extraction and classification is a crucial task to detect the stage of ictal (i.e. seizure period) and interictal (i.e. period between seizures) EEG signals for the treatment and precaution of the patient. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering their non-abrupt phenomena and inconsistency in different brain locations. In this paper, we present new approaches for feature extraction using high-frequency components from discrete cosine transformation (DCT) and intrinsic mode function (IMF) extracted from empirical mode decomposition (EMD). These features are then used as an input to least square-support vector machine (LV-SVM) to classify ictal and interictal EEG signals. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity, and accuracy with greater consistence of ictal and interictal period of epilepsy for benchmark dataset from different brain locations.

An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms

Epilepsy is a critical neurological disorder resulting from abnormal hyper-excitability of neurons in the brain. Studies have shown that epilepsy can be detected in electroencephalography (EEG) recordings of patients suffering from seizures. The performance of EEG-based epileptic seizure detection relies largely on how well one can extract features from an EEG that characterize seizure activity. Conventional feature extraction methods using time-series analysis, spectral analysis and nonlinear dynamic analysis have advanced in recent years to improve detection. The computational complexity has also increased to obtain a higher detection rate. This study aimed to develop an efficient feature extraction method based on Hjorth׳s mobility to reduce computational complexity while maintaining high detection accuracy. A new feature extraction method was proposed by computing the spectral power of Hjorth׳s mobility components, which were effectively estimated by differentiating EEG signals in real-time. Using EEG data in five epileptic patients, this method resulted in a detection rate of 99.46% between interictal and epileptic EEG signals and 99.78% between normal and epileptic EEG signals, which is comparable to most advanced nonlinear methods. These results suggest that the spectral features of Hjorth׳s mobility components in EEG signals can represent seizure activity and may pave the way for developing a fast and reliable epileptic seizure detection method.

Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals

Epilepsy is one of the common neurological disorders characterized by a sudden and recurrent malfunction of the brain that is termed seizure, affecting over 50 million individuals worldwide. The Electroencephalogram (EEG) is the most influential technique in detection of epileptic seizures. In recent years, many research works have been devoted to the detection of epileptic seizures based on analysis of EEG signals. Despite remarkable work on seizure detection, there is no generic seizure detection scheme which performs reasonably well for different patients and different brain locations. In this paper we present a generic approach for feature extraction of preictal (pre-stage of seizure onset) and interictal (period between seizures) EEG signals using empirical mode decomposition (EMD) along with discrete cosine transformation (DCT) by exploit temporal correlation for detection of preictal phase of epileptic seizure. Then least square support vector machine is applied on the features for classifications. Results demonstrate that our proposed method outperforms the state-of-the-art methods in terms of sensitivity, specificity and accuracy to classify preictal and interictal EEG signals to the benchmark dataset extracted from different brain locations of different patients. 

A Neural Network Model for Predicting Epileptic Seizures based on Fourier-Bessel Functions

To improve the social life of drug resistant epilepsy persons, a patient specific algorithm is needed that can predict seizures based on EEG with high sensitivity and specificity before the occurrence of a seizure. This algorithm predicting the seizure occurrence from Inter-ictal (seizure free) and pre-ictal (before seizure) transition. In this algorithm features are extracted by Fourier Bessel Expansion from inter-ictal and pre-ictal EEG signals. A neural network using back propagation algorithm is implemented for classification of epileptic states. The performance of algorithm is evaluated based on three measures, sensitivity, and specificity and classification accuracy. The results illustrate that the algorithm can predict seizures of two subjects before five minutes with an accuracy of 99.6%

Epileptic seizure detection by analyzing EEG signals using different transformation techniques

Feature extraction and classification are still challenging tasks to detect ictal (i.e., seizure period) and interictal (i.e., period between seizures) EEG signals for the treatment and precaution of the epileptic seizure patient due to different stimuli and brain locations. Existing seizure and non-seizure feature extraction and classification techniques are not good enough for the classification of ictal and interictal EEG signals considering for their non-abruptness phenomena, inconsistency in different brain locations, type (general/partial) of seizures, and hospital settings. In this paper we present generic seizure detection approaches for feature extraction of ictal and interictal signals using various established transformations and decompositions. We extract a number of statistical features using novel ways from high frequency coefficients of the transformed/decomposed signals. The least square support vector machine is applied on the features for classifications. Results demonstrate that the proposed methods outperform the existing state-of-the-art methods in terms of classification accuracy, sensitivity, and specificity with greater consistence for the large size benchmark dataset in different brain locations.

APPLICATION OF INTRINSIC TIME-SCALE DECOMPOSITION (ITD) TO EEG SIGNALS FOR AUTOMATED SEIZURE PREDICTION

Intrinsic time-scale decomposition (ITD) is a new nonlinear method of time-frequency representation which can decipher the minute changes in the nonlinear EEG signals. In this work, we have automatically classified normal, interictal and ictal EEG signals using the features derived from the ITD representation. The energy, fractal dimension and sample entropy features computed on ITD representation coupled with decision tree classifier has yielded an average classification accuracy of 95.67%, sensitivity and specificity of 99% and 99.5%, respectively using 10-fold cross validation scheme. With application of the nonlinear ITD representation, along with conceptual advancement and improvement of the accuracy, the developed system is clinically ready for mass screening in resource constrained and emerging economy scenarios.

Wavelet Based Features for Classification of Normal, Ictal and Interictal EEG Signals

Wavelet Based Features for Classification of Normal, Ictal and Interictal EEG Signals

Epileptic EEG classification based on extreme learning machine and nonlinear features

The automatic detection and classification of epileptic EEG are significant in the evaluation of patients with epilepsy. This paper presents a new EEG classification approach based on the extreme learning machine (ELM) and nonlinear dynamical features. The theory of nonlinear dynamics has been a powerful tool for understanding brain electrical activities. Nonlinear features extracted from EEG signals such as approximate entropy (ApEn), Hurst exponent and scaling exponent obtained with detrended fluctuation analysis (DFA) are employed to characterize interictal and ictal EEGs. The statistics indicate that the differences of those nonlinear features between interictal and ictal EEGs are statistically significant. The ELM algorithm is employed to train a single hidden layer feedforward neural network (SLFN) with EEG nonlinear features. The experiments demonstrate that compared with the backpropagation (BP) algorithm and support vector machine (SVM), the performance of the ELM is better in terms of training time and classification accuracy which achieves a satisfying recognition accuracy of 96.5% for interictal and ictal EEG signals.

Seizure Prediction With Spectral Power of EEG Using Cost-Sensitive Support Vector Machines

A patient-specific seizure prediction algorithm is proposed using a classifier to differentiate pre-ictal from inter-ictal EEG signals. The spectral power of EEG processed in four different fashions is used as features: raw, time-differential, space-differential, and time/space-differential EEG. The features are classified using cost-sensitive support vector machines by the double cross-validation methodology. The proposed algorithm has been applied to EEG recordings of 18 patients in the Freiburg EEG database, totaling 80 seizures and 437 h long inter-ictal recordings. Classification with the feature obtained from time/space-differential ECoG demonstrates the performance of 86.25% sensitivity and 0.1281 false positives per hour in out-of-sample testing.

A new method to estimate short-run and long-run interaction mechanisms in interictal state

Abstract Using different methods, most of the research articles on epilepsy analyze the structures of different neurological states (interictal, pre-ictal, ictal and post-ictal) to determine their distinguishing properties. On the other hand, some studies investigate the causal relationship between interictal state, pre-ictal and ictal state, especially in order to predict the seizures from the interictal EEG activities. This type of usage of the interictal data mathematically needs the imposition of some constraints which in turn may prevent researchers to extract more useful information hidden in the interictal EEG data. In the present study: firstly, we explore the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals we analyzed the revealed short-run and long-run interaction mechanisms of neuronal ensembles. Here we find: first, that short interictal series contains unit root and can be represented as autoregressive integrated moving average (ARIMA) process; second, between the interictal EEG signals there exists bidirectional causality (anthogonist effects) in the long-run. Therefore, in the long-run neither of the synchronized neuronal assemblies are positively affected (increasing amplitudes) from this relationship. Moreover, the long-run mechanism originated by co-movement (cointegration) of the interictal series reveals why there should not be a causal link from the interictal state to ictal state.

Relationship of Nonlinear Analysis, MRI and SPECT in the Lateralization of Temporal Lobe Epilepsy

Objectives: The purpose of this study was to investigate the correlation of lateralization by nonlinear analysis, magnetic resonance imaging (MRI) and interictal single-photon emission computed tomography (SPECT) in patients with temporal lobe epilepsy. Methods: Twenty-three patients (7 males, 16 females) were examined by MRI, interictal SPECT and EEG. Nonlinear dynamic properties of neuronal networks were estimated by calculating correlation dimensions on interictal EEG signals and corresponding surrogate data. Lateralization was detected based on the criteria introduced in this study. Concordance rates of the results among the three methods were compared. Results: Epileptogenic foci were shown in the temporal areas in 21 patients using the nonlinear method (8 left, 2 right, 11 both), while 20 patients showed abnormalities in temporal lobes on MR images (13 left, 5 right, 2 both). Low cerebral blood flows of the temporal lobes were detected in all patients (11 left, 8 right, 4 both). Completely concordant lateralization was observed in 8 patients (35%) for the nonlinear method and MRI, in 9 patients (39%) for the nonlinear method and SPECT, and in 10 patients (43%) for MRI and SPECT. There were no significant differences among the concordance rates for these different methods. Conclusions: Our results revealed that correlation dimension is useful for differentiating dynamic properties of neuronal networks in the interictal state, and can provide informative data for localizing epileptogenic foci in epileptic patients. Therefore, the present nonlinear method is recommended for use with patients during presurgical evaluation.

An autoregressive method for the measurement of synchronization of interictal and ictal EEG signals.

We propose a new measure of synchronization of multichannel ictal and interictal EEG signals. The measure is based on the residual covariance matrix of a multichannel autoregressive model. A major advantage of this measure is its ability to be interpreted both in the framework of stochastic and deterministic models. A preliminary analysis of EEG data from three patients using this measure documents the expected increased synchronization during ictal periods but also reveals that increased synchrony persists for prolonged periods (up to 2 h or more) in the postictal period.