Epileptic EEG Classification Research Articles

Epileptic EEG classification via deep learning-based strange attractor

Electroencephalography is commonly exploited in recording brains’ electrical activities for revealing the symptoms of various neurological diseases, e.g., epileptic seizures. We hypothesize that its capability primarily comes from the information it captures from the brain, which is supposed to be a chaotic dynamical system. Therefore, seizure detection in clinical practice could benefit from strange attractor reconstruction from time series in classifying non-ictal and ictal epileptic seizures. We propose a deep learning architecture and the loss function in a latent space to implement the reconstruction. By leveraging the proposed deep model, the hidden coordinates can be derived from a low-dimensional time series or univariate time series (e.g., Electroencephalography). Furthermore, added variables can extract more features from the reconstructed dynamical system. The proposed pipeline is evaluated by using a publicly available epileptic seizure database. Experimental results demonstrated the promising outcome of the proposed approach.

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern-Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10−4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.

Multiscale Hjorth Descriptor on Epileptic EEG Classification

The electroencephalogram (EEG) examination provides information on the brain’s electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was developed. Automatic epileptic EEG recognition is an area of interest on which much research focuses. The complexity approach to EEG signal analysis is interesting to be used as feature extraction, referring to the nonlinear characteristics of the signal. This study proposed an automatic epileptic EEG classification method based on the multiscale Hjorth descriptor measurement. EEG signals consisting of normal, interictal, and seizure (ictal) were simulated. The signal is scaled into new signals using the coarse-grained procedure on a scale of 1–20. Then, the Hjorth parameter which consists of activity, mobility, and complexity is calculated on the new signal. This process produces a feature vector that is used in the classification stage. Support vector machine (SVM) is used to evaluate the proposed feature extraction method. Simulation results showed that the Hjorth parameter on a scale of 1–15 yields 99.5% accuracy. The proposed method is expected to be applied to digital EEG for seizure detection and prediction.

Epileptic EEG Classification via Graph Transformer Network.

Deep learning-based epileptic seizure recognition via electroencephalogram signals has shown considerable potential for clinical practice. Although deep learning algorithms can enhance epilepsy identification accuracy compared with classical machine learning techniques, classifying epileptic activities based on the association between multichannel signals in electroencephalogram recordings is still challenging in automated seizure classification from electroencephalogram signals. Furthermore, the performance of generalization is hardly maintained by the fact that existing deep learning models were constructed using just one architecture. This study focuses on addressing this challenge using a hybrid framework. Alternatively put, a hybrid deep learning model, which is based on the ground-breaking graph neural network and transformer architectures, was proposed. The proposed deep architecture consists of a graph model to discover the inner relationship between multichannel signals and a transformer to reveal the heterogeneous associations between the channels. To evaluate the performance of the proposed approach, the comparison experiments were conducted on a publicly available dataset between the state-of-the-art algorithms and ours. Experimental results demonstrate that the proposed method is a potentially valuable instrument for epoch-based epileptic EEG classification.

Hybrid Attention Network for Epileptic EEG Classification.

Automatic seizure detection from electroencephalography (EEG) based on deep learning has been significantly improved. However, existing works have not adequately excavate the spatial-temporal information between EEG channels. Besides, most works mainly focus on patient-specific scenarios while cross-patient seizure detection is more challenging and meaningful. Regarding the above problems, we propose a hybrid attention network (HAN) for automatic seizure detection. Specifically, the graph attention network (GAT) extracts spatial features at the front end, and Transformer gets time features as the back end. HAN leverages the attention mechanism and fully extracts the spatial-temporal correlation of EEG signals. The focal loss function is introduced to HAN to deal with the imbalance of the dataset accompanied by seizure detection based on EEG. Both patient-specific and patient-independent experiments are carried out on the public CHB-MIT database. Experimental results demonstrate the efficacy of HAN in both experimental settings.

Analytic Representation vs. Angle Modulation of Hilbert Transform of Fast Walsh-Hadamard Coefficients (HTFWHC) in Epileptic EEG Classification

Analytic Representation vs. Angle Modulation of Hilbert Transform of Fast Walsh-Hadamard Coefficients (HTFWHC) in Epileptic EEG Classification

Classification of epileptic EEG based on improved empirical wavelet transform

Electroencephalography (EEG) is the most commonly used method in the diagnosis of epilepsy diseases. In order to identify epilepsy EEG signals more effectively, an automatic identification method of epilepsy EEG signals based on improved empirical wavelet transform (EWT) is proposed. Firstly, in view of the difficulty of spectral division in the EEG signal processing of epilepsy by empirical wavelet transform, an improvement measure is proposed, that is, the average difference spectrum of the signal is obtained to replace the signal spectrum in the empirical wavelet transform, and then a number of component signal with epileptic characteristic can be obtained from the original signal. Afterward, feature extraction and classification are completed through a common spatial pattern and AdaBoost algorithm. Simulation analysis was carried out on the Bonn epilepsy EEG data set, and the EEG signals of healthy people and epilepsy patients were identified and classified in the interictal and ictal periods, and high classification accuracy was achieved.

FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN

The EEG is one of the main medical instruments used by clinicians in the analysis and diagnosis of epilepsy through visual observations or computers. Visual inspection is difficult, time-consuming, and cannot be conducted in real time. Therefore, we propose a digital system for the classification of epileptic EEG in real time on a Field Programmable Gate Array (FPGA). The implemented digital system comprised a communication interface, feature extraction, and classifier model functions. The Hjorth descriptor method was used for feature extraction of activity, mobility, and complexity, with KNN was utilized as a predictor in the classification stage. The proposed system, run on a The Zynq-7000 FPGA device, can generate up to 90.74% accuracy in normal, inter-ictal, and ictal EEG classifications. FPGA devices provided classification results within 0.015 s. The total memory LUT resource used was less than 10%. This system is expected to tackle problems in visual inspection and computer processing to help detect epileptic EEG using low-cost resources while retaining high performance and real-time implementation.

Domain adaptive extreme learning machine for epileptic EEG classification

Epilepsy is a common brain disease, caused by abnormal discharge of human brain neurons, resulting in brain dysfunction syndrome. Although epilepsy does not have much impact on patients in the short term, but long-term frequent seizures can lead to physical and mental impact of patients. At present, the method used to detect epilepsy is to make a comprehensive judgment by EEG examination combined with clinical symptoms. With the application of AI technology, some advanced algorithms have been used to assist medical diagnosis. In this trend, we use extreme learning machine to observe and detect patients with epilepsy. ELM has the characteristics of high efficiency and high precision, so it is often used in regression and classification problems. However, in the face of different data sets, ELM structure is not enough to achieve good performance. This is caused by the uneven distribution of data in different data sets. To solve this problem, we add the transfer learning module to the basic ELM structure. The purpose of adding transfer learning is to divide the disordered data in the domain space and construct a data set suitable for ELM learning. Specifically, the raw data are mapped to high-dimensional space by kernel method through domain adaptive method. Secondly, in high-dimensional space, the distance between different domains should be reduced appropriately. Finally, ELM method is used to analyze and predict the changed data set. In the whole algorithm process, due to the characteristics of ELM updating weight, only a certain amount of hidden nodes are needed, and the training process is very fast. At the same time, after adding the transfer learning function module, the accuracy of ELM is also satisfactory. In this paper, the epilepsy data of patients were used for comparative experiments. The experimental results show that the method can maintain high efficiency and satisfactory accuracy.

A novel 2-piece rose spiral curve model: Application in epileptic EEG classification

This paper aimed to provide an innovative 2D phase space model and evaluate its performance in categorizing electroencephalogram (EEG) signals of normal and epileptic patients. The main contributions of the current study are as follows. (1) For the first time, it was proposed a new 2D model based on a 2-piece Rose Spiral Curve (RSC) in EEG analysis. (2) The trajectory patterns of the model were examined for signals of different natures, including constant, periodic, random, and EEG. (3) It was presented some benchmarks for quantifying the trajectory patterns. (4) Applying these measures, support vector machine, Naïve Bayes, AdaBoost, and K-nearest neighbor were used in the epileptic EEG classification problem to estimate the method efficiency. Bonn database, which takes account of EEG signals of healthy, in the course of an epileptic seizure occurrence, and seizure-free cases, was assessed. The results indicated that the proposed framework provided a correct rate of 100% for recognizing healthy subjects and the EEGs with seizure activity. Additionally, seizure-free brain activity was classified with an accuracy of 96.7%. To conclude, the proposed RSC model can be suitable for serving as a computer-aided diagnosis tool for epileptic seizures.

Combination of Coarse-Grained Procedure and Fractal Dimension for Epileptic EEG Classification

Epilepsy, cured by some offered treatments such as medication, surgery, and dietary plan, is a neurological brain disorder due to disturbed nerve cell activity characterized by repeated seizures. Electroencephalographic (EEG) signal processing detects and classifies these seizures as one of the abnormality types in the brain within temporal and spectral content. The proposed method in this paper employed a combination of two feature extractions, namely coarse-grained and fractal dimension, a challenge to obtain a highly accurate procedure to evaluate and predict the epileptic EEG signal of normal, interictal, and seizure classes. The result of classification accuracy using variance fractal dimension (VFD) and quadratic support machine vector (SVM) with a number scale of 10 is 99% as the highest one, excellent performance of the predictive model in terms of the error rate. In addition, a higher scale number does not determine a higher accuracy in this study.

Modeling of seizure and seizure-free EEG signals based on stochastic differential equations

• We proposed a novel signal modeling for EEG signals in order to detect seizure and seizure free intervals. • A major advantage of this model is that it does not require decomposing EEG signal to its spectral constructive rhythms. • Our results demonstrated promising performance of proposed model. • This model is computationally very simple and reliable and can be used in real time applications of epileptic EEG classification. • Furthermore, this type of statistical modeling can be used for various EEG segment classifications. seizures commonly occurs in epileptic patients and decrease their quality of life. Investigating past attacks and predict future seizures can be done by exact classification between healthy and seizure based segments in electroencephalograph (EEG) recordings of these patients. Modeling EEG signal can help to extract discriminative features from it. These features make automatic classification more accurate. In this paper we propose a new modeling for EEG signals based on stochastic differential equations (SDE). In this statistical modeling, EEG signals are modeled with a self-similar fractional Levy stable process due to their inherent self-similarity. These processes are considered as response of SDE to the zero mean white symmetric alpha stable noise and inversely, by applying a derivative operator on these processes this white noise could be obtained again. We use a scale invariant fractional derivative operator for this purpose. Having fitted a probability distribution to the histogram of EEG signal after derivation, parameters of fitted histogram can be applied as features for classification task. We modeled healthy and epileptic segments of EEG signal from Bonn University database, and Neurology and Sleep Centre of New Delhi database. As an application of proposed model, we used features obtained from modeled signals to train an SVM classifier. Experimental result revealed highest classification of 99.8% for Bonn University database and 99.1% for Sleep Centre of New Delhi database, between normal and epileptic EEG signals. In conclusion, the proposed model is simple (does not require any decomposition of EEG signals), accurate and computationally efficient.

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning.

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study.

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.

Epileptic Electroencephalogram Classification using Relative Wavelet Sub-Band Energy and Wavelet Entropy

Epilepsy is one of the common neurological disorders which can cause unprovoked seizures. Currently, diagnosis and evaluation are carried out using electroencephalogram (EEG) signal analysis, which is performed visually by clinicians. Since EEG signals tend to be random and non-stationary, the visual inspection often provides misrepresentation of results. Numerous studies have been proposed computer-based analysis for epileptic EEG classification; however, there is still a gap to improve detection accuracy with a small number of features. Therefore, in this study, we proposed an automatic detection protocol for epileptic EEG classification. The proposed methods are relative wavelet energy and wavelet entropy for feature extraction and combined with the classifier method for automatic detection. In this study, three classes of EEG consisted of pre-ictal, ictal, and interictal were used as test data and also evaluate the proposed method. EEG signals were decomposed using wavelet transform into five conventional sub-bands, including gamma, beta, alpha, theta, and delta. The relative energy and entropy were then calculated in each of these bands as a feature set. These methods are chosen with consider of low-cost computing. We tested the performance of our feature extraction method using Support Vector Machine (SVM), both linear and non-linear kernels. From the simulation, the highest accuracy was 80-96.7% for ictal vs. pre-ictal, ictal vs. inter-ictal, pre-ictal vs. inter-ictal, and ictal vs. non-ictal. Finally, this work was expected to help clinicians in the detection of epilepsy onset based on EEG signals.

Neural Network Based Epileptic EEG Detection and Classification

Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatment are available for epilepsy. These treatments involve use of medicines. But these are not effective in controlling frequency of seizure. There is need of removal of affected region using surgery. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is used before surgery for locating affected region. This manual process using EEG graphs is time consuming and requires deep expertise. In the present paper, a model has been proposed that preserves the true nature of EEG signal in form of textual one dimensional vector. The proposed model achieves a state of art performance for Bonn University dataset giving an average sensitivity, specificity of 81% and 81.4% respectively for classification among all five classes. Also for binary classification achieving 99.9%, 99.5% score value for specificity and sensitivity instead of 2D models used by other researchers. Thus developed system will significantly help neurosurgeons in increasing their performance.

Classification of Epileptic Seizure EEG Based on Fully Connected Neural Network

Classification of Epileptic Seizure EEG Based on Fully Connected Neural Network

AA Comparison of Dynamic Modeling Approaches for Epileptic EEG Detection and Classification.

Electroencephalogram (EEG) has been intensively used as a diagnosis tool for epilepsy. The traditional diagnostic procedure relies on a recording of EEG from several days up to a few weeks, and the recordings are visually inspected by trained medical professionals. This procedure is time consuming with a high misdiagnosis rate. In recent years, computer-aided techniques have been proposed to automate the epilepsy diagnosis by using machine learning methods to analyze EEG data. Considering the time-varying nature of EEG, the goal of this work is to characterize dynamic changes of EEG patterns for the detection and classification of epilepsy. Four different dynamic Bayesian modeling methods were evaluated using multi-subject epileptic EEG data. Experimental results show that an accuracy of 98.0% can be achieved by one of the four methods. The same method also provides an overall accuracy of 87.7% for the classification of seven different seizure types.

Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset.

In this paper, complexity analysis and dynamic characteristics of electroencephalogram (EEG) signal based on maximal overlap discrete wavelet transform (MODWT) has been exploited for the identification of seizure onset. Since wavelet-based studies were well suited for classification of normal and epileptic seizure EEG, we have applied MODWT which is an improved version of discrete wavelet transform (DWT). The selection of optimal wavelet sub-band and features plays a crucial role to understand the brain dynamics in epileptic patients. Therefore, we have investigated MODWT using four different wavelets, namely Haar, Coif4, Dmey, and Sym4 sub-bands until seven levels. Further, we have explored the potentials of six entropies, namely sigmoid, Shannon, wavelet, Renyi, Tsallis, and Steins unbiased risk estimator (SURE) entropies in each sub-band. The sigmoid entropy extracted from Haar wavelet in sub-band D4 showed the highest accuracy of 98.44% using support vector machine classifier for the EEG collected from Ramaiah Medical College and Hospitals (RMCH). Further, the highest accuracy of 100% and 94.51% was achieved for the University of Bonn (UBonn) and CHB-MIT databases respectively. The findings of the study showed that Haar and Dmey wavelets were found to be computationally economical and expensive respectively. Besides, in terms of dynamic characteristics, MODWT results revealed that the highest energy present in sub-bands D2, D3, and D4 and entropies in those respective sub-bands outperformed other entropies in terms of classification results for RMCH database. Similarly, using all the entropies, sub-bands D5 and D6 outperformed other sub-bands for UBonn and CHB-MIT databases respectively. In conclusion, the comparison results of MODWT outperformed DWT.

Spectral information of EEG signals with respect to epilepsy classification

BackgroundThe spectral information of the EEG signal with respect to epilepsy is examined in this study.MethodIn order to assess the impact of the alternative definitions of the frequency sub-bands that are analysed, a number of spectral thresholds are defined and the respective frequency sub-band combinations are generated. For each of these frequency sub-band combination, the EEG signal is analysed and a vector of spectral characteristics is defined. Based on this feature vector, a classification schema is used to measure the appropriateness of the specific frequency sub-band combination, in terms of epileptic EEG classification accuracy.ResultsThe obtained results indicate that additional frequency band analysis is beneficial towards epilepsy detection.ConclusionsThis work includes the first systematic assessment of the impact of the frequency sub-bands to the epileptic EEG classification accuracy, and the obtained results revealed several frequency sub-band combinations that achieve high classification accuracy and have never been reported in the literature before.