Identification Of Epileptic Seizures Research Articles

Quadrature Response Spectra Deep Neural Based Behavioral Pattern Analytics for Epileptic Seizure Identification

Abstract The brain’s Electroencephalogram (EEG) signals contain essential information about the brain and are widely used to support the analysis of epilepsy. By analyzing brain behavioral patterns, an accurate classification of different epileptic states can be made. The behavioral pattern analysis using EEG signals has become increasingly important in recent years. EEG signals are boisterous and non-linear, and it is a demanding mission to design accurate methods for classifying different epileptic states. In this work, a method called Quadrature Response Spectra-based Gaussian Kullback Deep Neural (QRS-GKDN) Behavioral Pattern Analytics for epileptic seizures is introduced. QRS-GKDN is divided into three processes. First, the EEG signals are preprocessed using the Quadrature Mirror Filter (QMF) and the Power Frequency Spectral (PFS) and Response Spectra (RS)-based Feature Extraction is applied for Behavioral Pattern Analytics. The QMF function is applied to the preprocessed EEG input signals. Then, relevant features for behavioral pattern analysis are extracted from the processed EEG signals using the PFS and RS function. Finally, Gaussian Kullback–Leibler Deep Neural Classification (GKDN) is implemented for epileptic seizure identification. Furthermore, the proposed method is analyzed and compared with dissimilar samples. The results of the Proposed method have superior prediction in a computationally efficient manner for identifying epileptic seizure based on the analyzed behavioral patterns with less error and validation time.

Enhanced Epileptic Seizure Identification using Sparse ELM-ABO Fusion with Feature Reduction and Multi-class Classification

The interpretation of the electroencephalogram (EEG) signal is one method that can be utilized to diagnose epilepsy, which is one of the most prevalent brain illnesses. The length of an EEG signal is typically quite long, making it difficult to interpret manually. Extreme Learning Machine (ELM) is used to detection of Epilepsy and Seizure. But in ELM Storage space and training time is high. In order to reduce training time and storage space African Buffalo Optimization (ABO) algorithm is used. ABO is combined with Sparse ELM to improve the speed, accuracy of detection and reduce the storage space. First, Wavelet transform is used to extract relevant features. Due to their high dimensionality, these features are then reduced by using linear discriminant analysis (LDA). The proposed Hybrid Sparse ELM technique is successfully implemented for diagnosing epileptic seizure disease. For classification, the Sparse ELM-ABO classifier is applied to the UCI Epileptic Seizure Recognition Data Set training dataset, and the experimental findings are compared to those of the SVM, Sparse ELM, and ELM classifiers applied to the same database. The proposed model was tested in two scenarios: binary classification and multi-label classification. Seizure identification is the only factor in binary classification. Seizure and epilepsy identification are part of multi-label classification. It is observed that the proposed method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as prediction accuracy, specificity, precision, recall and F-score. Binary classification scores 96.08%, while multi-label classification achieves 90.89%.

Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection

The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence.

Generalized Epileptic Seizure Prediction using Machine Learning Method

In recent years, the electroencephalography (EEG) signal identification of epileptic seizures has developed into a routine procedure to determine epilepsy. Since physically identifying epileptic seizures by expert neurologists becomes a labor-intensive, time-consuming procedure that also produces several errors. Thus, efficient, and computerized detection of epileptic seizures is required. The disordered brain function that causes epileptic seizures can have an impact on a patient's condition. Epileptic seizures can be prevented by medicine with great success if they are predicted before they start. Electroencephalogram (EEG) signals are utilized to predict epileptic seizures by using machine learning algorithms and complex computational methodologies. Furthermore, two significant challenges that affect both expectancy time and genuine positive forecast rate are feature extraction from EEG signals and noise removal from EEG signals. As a result, we suggest a model that offers trustworthy preprocessing and feature extraction techniques. To automatically identify epileptic seizures, a variety of ensemble learning-based classifiers were utilized to extract frequency-based features from the EEG signal. Our algorithm offers a higher true positive rate and diagnoses epileptic episodes with enough foresight before they begin. On the scalp EEG CHB-MIT dataset on 24 subjects, this suggested framework detects the beginning of the preictal state, the state that occurs before a few minutes of the onset of the detention, resulting in an elevated true positive rate of (91%) than conventional methods and an optimum estimation time of 33 minutes and an average time of prediction is 23 minutes and 36 seconds. Depending on the experimental findings’ The maximum accuracy, sensitivity, and specificity rates in this research were 91 %, 98%, and 84%.

RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals.

Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram's (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model's efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques.

Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces.

Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is acknowledged as a key hindrance to BCI, and a new strategy based on domain generalization has recently evolved to address it. In light of this, we've concentrated on developing an EEG classification framework that can be applied directly to data from unknown domains (i.e. subjects), using only data acquired from separate subjects previously. For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor. Our experiments showed that using the domain information as an auxiliary network increases the generalization performance. Clinical relevance-This study suggests a strategy to improve the performance of the subject-independent BCI systems. Our framework can help to reduce the need for further calibration and can be utilized for a range of mental state monitoring tasks (e.g. neurofeedback, identification of epileptic seizures, and sleep disorders).

EEG signal based seizure detection focused on Hjorth parameters from tunable-Q wavelet sub-bands

In recent years, automated seizure identification with electroencephalogram (EEG) signals has received considerable attention and appears to be an appropriate approach for diagnosis and treatment of the disease. This paper analyze the ability of Hjorth parameter for seizure detection using EEG signals. The tunable-Q wavelet transform (TQWT) is applied to decompose an EEG signal into various subbands at different levels. The Hjorth parameters namely activity, mobility, and complexity are studied over the decomposed components. The University of Bonn, Germany dataset is studied to validate the proposed method with including seizure, seizure-free, and normal categories of EEG signal. Classification findings show that the proposed technique with estimating the Hjorth parameters preserves efficiency and is appropriate for automated identification of epileptic seizures. In this work, very high classification accuracy is achieved in various set of combinations. The proposed technique is compared with state-of-the-art approaches available in the literature.

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals.

Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation.

Epileptic seizure identification in EEG signals using DWT, ANN and sequential window algorithm

A patient-specific novel systematic methodology is described in this study for automatic seizure detection from raw electroencephalogram (EEG) signals. Filtering process by means of band-pass finite impulse response (FIR) filter with the frequency range of 0.5–40 Hz is implemented at the outset to eliminate different artifacts and noises mixed with raw EEG signals. As EEGs are highly non-linear and non-stationary signals in nature, discrete wavelet transform (DWT) is then used to analyze the signals in time-frequency domain. DWT with four level decomposition is performed using db6 mother wavelet for feature extraction. A new feature set, composed of eleven non-linear statistical features extracted from each sub-bands resulting from due to wavelet decomposition, is then fed to the input of artificial neural network (ANN) to classify the signal accurately. Finally, a novel algorithm named sequential window algorithm is carried out to improve the classification performance. 99.44% mean classification accuracy, 80.66% average sensitivity, 4.12 s mean latency and 0.2% average false positive rate (FPR) are achieved in this study. This study successfully reduces the latency time with more accuracy and significantly low FPR.

ESIMD: Epileptic seizure identification using metaheuristic deep learning technique

AbstractThe identification of epileptic seizure from electroencephalogram (EEG) segments is significant for seizure identification and classification. In epilepsy, the patients develop epileptic seizures. These lead to neurological disorders and abnormal brain activities. The manual identification of seizures is not very efficient. As the manual analysis of EEG data requires expert neurologists it is prone to inaccuracies. Therefore, automated methods for seizure identification are required. Deep learning methods have shown efficiency in the seizure classification problem. Many challenges are faced by the existing methods owing to the properties of the EEG signals like transiency, non‐stationary behaviour and presence of noise. Therefore, to overcome these challenges and design a highly efficient method an automated method based on deep learning and spotted hyena optimization (SHO) algorithm is proposed. The SHO algorithm is used to initialize the network as it avoids the local minima problem. Epileptic seizure identification using metaheuristic deep learning (ESIMD) is proposed in this paper which is a hybridization of spotted hyena and deep convolution network. The convolutional neural network used has two blocks, the first block is used for feature extraction and the second block for seizure classification. ESIMD is used for seizure detection and classification. The proposed algorithm can handle the complex and large number of features in EEG signals. The performance of the proposed is evaluated by applying it on EEG Bonn Dataset. The results are compared with 13 other conventional methods. The comparative study shows that ESIMD gives higher accuracy than the existing methods.

Designing Efficient NoC-Based Neural Network Architectures for Identification of Epileptic Seizure

Designing Efficient NoC-Based Neural Network Architectures for Identification of Epileptic Seizure

Yükseltme Sınıflandırıcıları kullanarak EEG Sinyaline dayalı Epileptik Nöbet Tespiti

The detection of epileptic seizures by electroencephalography (EEG) signals has become a standard method for the diagnosis of epilepsy. Accurate and automatic detection of epileptic seizures is needed since manual identification of epileptic seizures by specialist neurologists is a time consuming and labor intensive process, which also leads to various errors. For this purpose, frequency-based features were extracted from the EEG signal and a various classifiers based on ensemble learning was used to detect epileptic seizures automatically. The performance of the proposed method was tested using cross-validation and cross-patient experiments. According to the experimental results, sensitivity, specificity and accuracy rates were 94%, 93% and 93% for cross-validation and 76%, 90% and 90% for cross-patients, respectively.

An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis.

Epilepsy is a disease recognized as the chronic neurological dysfunction of the human brain which is described by the sudden and excessive electrical discharges of the brain cells. Electroencephalogram (EEG) is a prime tool applied for the diagnosis of epilepsy. In this study, a novel and effective approach is introduced to decompose the non-stationary EEG signals using the Fourier decomposition method. The concept of position, velocity, and acceleration has been employed on the EEG signals for feature extraction using [Formula: see text] norms computed from Fourier intrinsic band functions (FIBFs). The proposed scheme comprises three main sections. In the first section, the EEG signal is decomposed into a finite number of FIBFs. In the second stage, the features are extracted from FIBFs and relevant features are selected by using the Kruskal-Wallis test. In the last stage, the significant features are passed on to the support vector machine (SVM) classifier. By applying 10-fold cross-validation, the proposed method provides better results in comparison to the state-of-the-art methods discussed in the literature, with an average classification accuracy of 99.96% and 99.94% for classification of EEG signals from the BONN dataset and the CHB-MIT dataset, respectively. It can be implemented using the computationally efficient fast Fourier transform (FFT) algorithm.

Majority voting-based hybrid feature selection in machine learning paradigm for epilepsy detection using EEG

This article presents a combination of statistical and discrete wavelet transform (DWT)-based features for the identification of epileptic seizures in electroencephalogram (EEG) signals. A total of 150 quantitative features are extracted from EEG signals. A multi-criteria hybrid feature selection is proposed by combining six feature ranking methods using the majority voting technique to identify the most relevant EEG markers. Kernel-based support vector machine is used to evaluate the proposed approach along with a hybrid classifier namely support vector neural network (SVNN) which is a combination of support vector machine (SVM) and artificial neural network (ANN). For performance evaluation of the proposed method, a benchmarked database is used. A comparative study of various types of SVM and SVNN with ten-fold and hold-out cross-validation techniques is conducted. The highest classification accuracy (CA) of 98.18% and 100% sensitivity is achieved with a fine Gaussian SVM classifier with hold-out data division protocol.

Majority voting-based hybrid feature selection in machine learning paradigm for epilepsy detection using EEG

This article presents a combination of statistical and discrete wavelet transform (DWT)-based features for the identification of epileptic seizures in electroencephalogram (EEG) signals. A total of 150 quantitative features are extracted from EEG signals. A multi-criteria hybrid feature selection is proposed by combining six feature ranking methods using the majority voting technique to identify the most relevant EEG markers. Kernel-based support vector machine is used to evaluate the proposed approach along with a hybrid classifier namely support vector neural network (SVNN) which is a combination of support vector machine (SVM) and artificial neural network (ANN). For performance evaluation of the proposed method, a benchmarked database is used. A comparative study of various types of SVM and SVNN with ten-fold and hold-out cross-validation techniques is conducted. The highest classification accuracy (CA) of 98.18% and 100% sensitivity is achieved with a fine Gaussian SVM classifier with hold-out data division protocol.

Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise

Background: Epileptic seizure detection is traditionally performed by visual observation of Electroencephalogram (EEG) signals. Owing to its onerous and time-consuming nature, seizure detection based on visual inspection hinders epilepsy diagnosis, monitoring, and large-scale data analysis in epilepsy research. So, there is a dire need of an automatic seizure detection scheme.Method: An automated scheme for epileptic seizure identification is developed in this study. Here we utilize a signal processing technique, namely-complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for epileptic seizure identification. First, we decompose segments of EEG signals into intrinsic mode functions by CEEMDAN. The mode functions are then modeled by normal inverse Gaussian (NIG) pdf parameters. In this work, NIG modeling is employed in conjunction with CEEMDAN for epileptic seizure detection for the first time. The efficacy of the NIG parameters in the CEEMDAN domain is demonstrated by intuitive, graphical, and statistical analyses. Adaptive Boosting, an eminent ensemble learning based classification model, is implemented to perform classification.Results: Experimental outcomes suggest that the algorithmic performance of the proposed scheme is promising in all the cases of clinical significance. Comparative evaluation of algorithmic performance with the state-of-the-art schemes manifest that the seizure detection scheme proposed herein outperforms competing algorithms in terms of accuracy, sensitivity, specificity, and Cohen’s Kappa coefficient.Conclusions: Upon its implementation in clinical practice, the proposed seizure detection scheme will eliminate the onus of medical professionals and expedite epilepsy research and diagnosis.

MSBE Analysis with Frequency Spectra for Automated Identification of Epileptic Seizure

Objective-This study introduces a reliable automated seizure detection technique based on MSBE (Multi scale bubble entropy) and frequency spectral analysis. Method- This paper aims to develop a novel seizure detection technique that incorporates AM FM model for decomposition of EEG into different sub bands. In our approach, integrated feature set is constructed using multi scale bubble entropy analysis at each sub band and frequency spectral analysis at each electrode. Result-In this paper, an application of bubble entropy with different frequency parameter such as PPF and PSD is provided in order to access its stable and outstanding performance on epileptic seizer detection. The experimental results show that classification accuracy is improved with this algorithm. These finding suggest that extracted features can be used for treatment of epilepsy. Significance- This method provides greater stability and discriminative power, so this technique could be used to detect wider range of seizures.

Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal, interictal and ictal electroencephalogram (EEG) signals. In this study we propose a novel method for automatic identification of epileptic seizures in singe-channel EEG signals based upon time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first two PRCs of the EEG signals are extracted, which contain most of the EEG signals’ energy and are considered to be the predominant PRCs. Second, four levels DWT is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected for analysis. Third, phase space of the PRCs is reconstructed based on db3, in which the properties associated with the nonlinear EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between normal, interictal and ictal EEG signals. Fourth, neural networks are then used to model, identify and classify EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Finally, experiments are carried out on the University of Bonn’s widely used and publicly available epilepsy dataset to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved average classification accuracy for eleven cases is reported to be 98.15%. Compared with many state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of seizure EEG signals in the clinical application.

Epileptic seizure identification using entropy of FBSE based EEG rhythms

This paper has proposed a new method for classification of epileptic seizures based on weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier–Bessel series expansion (FBSE) of electroencephalogram (EEG) signals. In this method, each EEG signal is considered for the FBSE to determine the coefficients then the selected order values of these coefficients are used to obtain the rhythms (δ, θ, α, β, and γ). The feature used in this work is WMRPE which has been extracted from rhythms of EEG signal. These computed feature values are then used in distinct classifiers such as random forest (RF), least squares support vector machine (LS-SVM), and regression for classification of epileptic seizure EEG signals. The classification is validated using 10-fold cross-validation technique in order to verify the robustness of the proposed work and the optimization of classification performance has been implemented with feature ranking methods. The proposed method is also tested with additive white Gaussian noise (AWGN) at different signal to noise ratio (SNR) levels. In this present work, the proposed method provides better classification accuracy results for seven different classification problems in which six are two-class classification problems and the remaining one is three-class classification problem. In two-class classification problems, the EEG signals of epileptic patients at the time of seizure interval has been classified with six distinct classes of EEG signals i.e., healthy subjects with eyes open condition, healthy subjects with eyes closed condition, epileptic patients at the time of seizure-free interval captured from hippocampal formation zone, epileptic patients at the time of seizure-free interval captured from epileptogenic zone, epileptic patients at the time of seizure-free interval, and subjects at the time of non-seizure (healthy and seizure-free) interval. In three-class classification problem, the EEG signals corresponding to epileptic patients during seizure interval, healthy subjects with eyes open condition, and epileptic patients at the time of seizure-free interval captured from hippocampal formation zone have been classified with the help of proposed method. The obtained results show that the WMRPE with FBSE based rhythms provides better classification accuracies in comparison to the existing rhythms based method and other existing methods. The proposed method is also perform well in noisy environment at different SNR levels.

A review on the pattern detection methods for epilepsy seizure detection from EEG signals.

Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians' encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics. This consecutively necessitates the identification of the pattern recognition technique to efficiently differentiate EEG epileptic data from the EEG data of various conditions.