Epileptic EEG Detection Research Articles

An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network.

Epilepsy, an enduring neurological disorder, afflicts approximately 65 million individuals globally, significantly impacting their physical and mental wellbeing. Traditional epilepsy detection methods are labor-intensive, leading to inefficiencies. Although deep learning techniques for brain signal detection have gained traction in recent years, their clinical application advancement is hindered by the significant requirement for high-quality data and computational resources during training. The neural network training initially involved merging two datasets of different data quality, namely Bonn University datasets and CHB-MIT datasets, to bolster its generalization capabilities. To tackle the issues of dataset size and class imbalance, we employed small window segmentation and Synthetic Minority Over-sampling Technique (SMOTE). algorithms to augment and equalize the data. A streamlined neural network architecture was then proposed, drastically reducing the model's training parameters. Notably, a model trained with a mere 9,371 parameters yielded impressive results. The three-classification task on the combined dataset delivered an accuracy of 98.52%, sensitivity of 97.99%, specificity of 99.35%, and precision of 98.44%. The experimental findings of this study underscore the superiority of the proposed method over existing approaches in both model size reduction and accuracy enhancement. As a result, it is more apt for deployment in low-cost, low computational hardware devices, including wearable technology, and various clinical applications. Clinical and Translational Impact Statement- This study is a Pre-Clinical Research. The lightweight neural network is easily deployed on hardware device for real-time epileptic EEG detection.

Automatic Seizure Detection Based on Stockwell Transform and Transformer.

Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.

Automated detection of epileptic EEG signals using recurrence plots-based feature extraction with transfer learning

Automated detection of epileptic EEG signals using recurrence plots-based feature extraction with transfer learning

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

BackgroundEpilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results.MethodsIn order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals.ResultsThe empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%.ConclusionThe proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection

The EEG is an indispensable tool for clinical diagnosis of neurological disorders. The issue that how to correctly decoding the pathological information from EEG has been attracting a lot of interest. This paper creates a robust feature extraction scheme based on synchrosqueezing short-time Fourier transform (SSTFT) and graph regularized non-negative matrix factorization (GNMF), which is applied to epileptic EEG detection. The SSTFT is employed to analyze the EEG in time–frequency domain, followed by a GNMF decomposition that maps these components into a low-dimensional representation where latent clues are easily emphasized and learned, and finally the logarithmic energy, computed as features, is fed to a fuzzy K-Nearest Neighbor classifier for the purpose of discriminating seizure events. Our method is evaluated using two public databases including Bonn database and CHB-MIT database. Obtained results confirm the superiority of the proposed features by achieving classification accuracy of at least 98.0% for both short-term and long-term EEG recordings. It is expected to offer a new thought and perspective for the study and design of the automatic EEG monitoring system.

EEpileptic electroencephalogram signal classification method based on elastic variational mode decomposition

Epilepsy is an extensive nervous system disease nowadays. Electroencephalogram (EEG) can capture the abnormal discharge of nerves in the brain duration of seizure and provide a non-invasive way to identify epileptogenic sites in the brain. In order to distinguish between focal epilepsy EEG signal and non-focal epilepsy EEG signal, in this paper we propose an automated epileptic EEG detection method based on the elastic variational mode decomposition (EVMD). The proposed EVMD algorithm is a method of analyzing the signals and also a processing method in time-frequency domain, in which the elastic net regression is used to reconstruct a constrained variational model in variational mode decomposition (VMD). Used in the VMD algorithm is the Tikhonov regularization that is also statistically called ridge regression as a solution of recovering the unknown signal and assessing the bandwidth of a mode, namely the variational equation constructed by VMD only has L2 norm. However, the ridge regression cannot select variables when the equation has multiple variables. Another regression method, called lasso regression, only has L1 norm and can select a more accurate model from multiple variables, but it has worse performance when variables have group effect or co-linearity. The elastic net regression has advantages of ridge regression and lasso regression, in other word, the variational equation constructed by EVMD has both L1 regularization item and L2 regularization item, so in this paper we propose the EVMD by elastic net regression. In addition, in this paper the EVMD is used to distinguish between focal epilepsy EEG signal and non-focal epilepsy EEG signal. Firstly, the original EEG signals are divided into several sub-signals where the test signals are divided into sub-signals with shorter durations by time series and a reasonable time overlap is kept between successive sub-signals. After that each sub-signal is decomposed into intrinsic mode functions by using the EVMD. Furthermore, the refined composite multiscale dispersion entropy (RCMDE) as feature is extracted from each intrinsic mode function where a Student’s t-test is used to assess the statistical differences between RCMDEs extracted from focal and non-focal EEG signals respectively. Finally, the support vector machine (SVM) is used to classify their features. For an epilepsy EEG signalspublic data set, the final experimental results show that the performance indices of accuracy, sensitivity, and specificity can reach 92.54%, 93.22% and 91.86% respectively.

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.

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.

Classification of epilepsy period based on combination feature extraction methods and spiking swarm intelligent optimization algorithm

SummaryEpilepsy seriously damages the physical and mental health of patients. Detection of epileptic EEG signals in different periods can help doctors diagnose the disease. The change of frequency components during epilepsy seizures is obvious, and there may be noises in epilepsy EEG signals. Moreover, epileptic seizures are closely related to the release of neuronal spiking in the brain. In this paper, we propose an approach for epilepsy period classification based on combination feature extraction methods and spiking swarm intelligent optimization classification algorithm. First, combination feature extraction methods take in account both the time‐frequency features and principal component features of epilepsy. The time‐frequency features are obtained by WPT or STFT‐PSD, and noises are removed while extracting principal component features by PCA. Second, spiking swarm intelligent optimization classification algorithm takes advantage of individual cooperation and information interaction with strong robustness. Its simulated neurons are closer to reality, which consider more information and obtain stronger computing power. The experimental results show that the average classification accuracy of the proposed method can reach 98.95% and the highest classification accuracy can reach 100%. Compared with other methods, the proposed method has the best classification performance.

간질 환자 분류를 위한 DNN 기반 간질 뇌파 검출 시스템

간질은 발작으로 온몸이 꼬이고 입에 거품을 물며 의식을 잃는 뇌 질환으로 전 세계적으로 4~5천만에 이르는 인구가 앓고 있는 흔한 질병이다. 간질은 예측이 불가능하기 때문에 간질 환자는 항상 신체 손상의 위험에 노출되어 있다. 따라서 간질 환자를 발작 전에 분류해내어 사고를 예방하는 것이 중요하다. 본 연구에서는 간질 환자를 진단하기 위한 DNN 기반 간질 뇌파 검출 시스템을 제안한다. 정상인과 간질 환자의 뇌파 데이터를 사용하여 연구를 진행하였다. 데이터의 불균형 문제를 해결하기 위해 ADASYN 기법을 사용하였고, IPCA 기법을 사용하여 간질 뇌파 검출 시에 필요한 중요한 특징들을 추출하였다. 추출된 특징들을 DNN기반의 4가지 최적화 알고리즘에 적용하여 모델을 구축하고 성능평가를 진행하였다. 실험 결과 Nadam 알고리즘을 사용한 최적화 모델이 97.6%의 정확도와 0.997의 AUC 값으로 가장 높은 성능을 나타냈다. 이 최적화 모델을 사용하여 의사는 뇌파검사 시에 높은 정확도로 간질 환자를 분류할 수 있을 것이다.

Recognition of Multiclass Epileptic EEG Signals Based on Knowledge and Label Space Inductive Transfer.

Electroencephalogram (EEG) signal recognition based on machine learning models is becoming more and more attractive in epilepsy detection. For multiclass epileptic EEG signal recognition tasks including the detection of epileptic EEG signals from different blends of different background data and epilepsy EEG data and the classification of different types of seizures, we may perhaps encounter two serious challenges: (1) a large amount of EEG signal data for training are not available and (2) the models for epileptic EEG signal recognition are often so complicated that they are not as easy to explain as a linear model. In this paper, we utilize the proposed transfer learning technique to circumvent the first challenge and then design a novel linear model to circumvent the second challenge. Concretely, we originally combine γ -LSR with transfer learning to propose a novel knowledge and label space inductive transfer learning model for multiclass EEG signal recognition. By transferring both knowledge and the proposed generalized label space from the source domain to the target domain, the proposed model achieves enhanced classification performance on the target domain without the use of kernel trick. In contrast to the other inductive transfer learning methods, the method uses the generalized linear model such that it becomes simpler and more interpretable. Experimental results indicate the effectiveness of the proposed method for multiclass epileptic EEG signal recognition.

Design of Gm-C wavelet filter for on-line epileptic EEG detection

Design of Gm-C wavelet filter for on-line epileptic EEG detection

Automatic seizure detection based on kernel robust probabilistic collaborative representation.

Visual inspection of electroencephalogram (EEG) recordings for epilepsy diagnosis is very time-consuming. Therefore, much research is devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this study, a kernel version of the robust probabilistic collaborative representation-based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals. The kernel R-ProCRC jointly maximizes the likelihood that a test EEG sample belongs to each of the two classes (seizure and non-seizure), and uses the kernel function method to map the EEG samples into the higher dimensional space to relieve the problem that they are linearly non-separable in the original space. The wavelet transform with five scales is first employed to process the raw EEG signals. Next, the test EEG samples are collaboratively represented on the training sets by the kernel R-ProCRC and they are categorized by checking which class has the maximum likelihood. Finally, post-processing is deployed to reduce misjudgment and acquire more stable results. This method is evaluated on two EEG databases and yields an accuracy of 99.3% for interictal and ictal EEGs on the Bonn database. In addition, the average sensitivity of 97.48% and specificity of 96.81% are achieved from the Freiburg database. Graphical abstract Visual inspection of EEG recordings for epilepsy diagnosis is very time-consuming. Therefore, many researchers are devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this paper, a kernel version of the robust probabilistic collaborative representation based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals. The kernel R-ProCRC jointly maximizes the likelihood that a test EEG sample belongs to each of the two classes, i.e., seizure and non-seizure, and uses the kernel function method to map the EEG samples into the higher dimensional space to relieve the problem that they are linearly non-separable in the original space. The main procedures of the proposed method are exhibited in the two figures as following, Fig. 1 The main procedures of the proposed method. (a) The schematic diagram of EEG classification based on the Freiburg database. (b) The detailed procedures of the kernel R-ProCRC This method has been evaluated on two different types of EEG databases and shows superior performance.

Performance analysis of power and power variance for classification, detection and localization of epileptic multi-channel EEG

There are a large number of data sets of EEG signal for which, it is difficult to judge and monitor brain activity through observations. Epilepsy is a disorder in which a recurrent and sudden malfunction of the brain is characterized. It is proposed to classify, detect and localize Epileptic multi-channel EEG through various power and novel power variance features non-invasively. This work presents power spectral estimation (PSE) using time–frequency analysis of EEG signals in both parametric (FFT) and non-parametric methods (i.e. Welch, Burg, Covariance, MUSIC and Yule–Walker). To examine the robustness of power features for different methods, the analysis of p value is performed. The detection of epileptic seizure is classified using different kernels through SVM. It is observed from the PSE that the power features have higher values in epileptic subjects as compared to non-epileptic subjects. Amongst all the parametric and non-parametric methods, the MUSIC method gives the highest average power. Sensitivity, specificity, and classification accuracy are 100% for Welch, Burg, Covariance, and Yule–Walker methods while MUSIC and FFT methods deliver 98.73 and 99.52% respectively. The novelty is introduced through the quantification of power and power variance robust feature region/lobe-wise. This quantification is used for the localization of 25 epileptic subjects. Analysis of the parametric and non-parametric PSD methods for extraction of power and power variance features is not used by any study. These are effectively utilized for detection and localization of epilepsy non-invasively.

Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG

Abstract Background The Electroencephalogram (EEG) signal is a time series depictive signal that contains the useful knowledge about the state of the brain. It has high temporal resolution for detection of chronic brain disorders such as epilepsy/seizure, dementia, sleep apnea, schizophrenia, etc. In this work, EEG is a prime concern for seizure/epilepsy detection and localization. Methods Entropy estimator is a good solution to this problem. Here, the time series complexity analysis of brain signal is carried using five different entropy estimators: Shannon Entropy, Renyi Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy. The average entropy values of EEG signal is significantly found lower for epileptic data sets compared to non-epileptic EEG. Results Experimental results evaluated for discriminating ability of each entropy measure demonstrated that among all entropies, Fuzzy Entropy discriminates between epileptic EEG and non-epileptic EEG more efficiently with a minimum p-value (0.001) compared to other four entropy estimators. Fuzzy Entropy defines the similarity between two vectors fuzzily on the basis of exponential function. Unlike to Approximate and Sample Entropy, the Fuzzy Entropy is free from parameter limitations and offers efficient results even for the small tolerance (r Conclusion The Fuzzy entropy gives better stability and consistency with highest discriminating ability compared to other entropy estimators. Eventually, results for detection and localization of epilepsy for affected channel and region through the variance and FuzzyEn are cross-validated by expert Neuro-physician.

Automatic epileptic EEG detection using DT-CWT-based non-linear features

Abstract The epilepsy is a type of common neurological disorder plaguing many people around the world. A novel method based on the dual-tree complex wavelet transform (DT-CWT), in this study, is proposed to develop a reliable diagnosis method for the epileptic EEG detection. We explore the ability of DT-CWT to decompose the original EEG into five constituent sub-bands, which are associated with non-linear features such as the Hurst exponent (H), Fractal Dimension (FD) and Permutation Entropy (PE). Furthermore, influences of different filter types on the DT-CWT are considered in this study as well. With these features, the support vector machine (SVM) configured with filters of the near-symmetric 13/19 tap filters (NS 13/19) and Q-shift 14/14 tap filters (QS 14/14) is found to achieve the preferable classification accuracy of 98.87%, which is visibly higher than that with discrete wavelet transform (DWT)-based features. Results demonstrate that the technique proposed by us can not only provide significant performance with less computational cost but also can implement simply. It will be a potential method for practical applications extended to the development of a real-time brain monitoring system.

Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification.

An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.

Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

Novel feature extraction method based on weight difference of weighted network for epileptic seizure detection.

The extraction method of classification feature is primary and core problem in all epileptic EEG detection algorithms, since it can seriously affect the performance of the detection algorithm. In this paper, a novel epileptic EEG feature extraction method based on the statistical parameter of weighted complex network is proposed. The EEG signal is first transformed into weighted network and the weight differences of all the nodes in the network are analyzed. Then the sum of top quintile weight differences is extracted as the classification feature. At last, the extracted feature is applied to classify the epileptic EEG dataset. Experimental results show that the single feature classification based on the extracted feature obtains higher classification accuracy up to 94.75%, which indicates that the extracted feature can distinguish the ictal EEG from interictal EEG and has great potentiality of real-time epileptic seizures detection.

Automatic detection of epileptic EEG based on recurrence quantification analysis and SVM

Automatic detection and classification of epileptic EEG signals have been a significance method for the clinical diagnosis and treatment of epilepsy. The recurrence quantification analysis (RQA) based on the recurrence plot could visualize the recurrence behaviors of dynamical systems from the nonlinear time series and analysis of the recurrence properties. This paper presents a new feature extraction method for epileptic EEG signals based on the recurrence quantification analysis. Experimental results show that the seizure detection directly based on recurrence quantification analysis features has a higher detection performance; especially the classification accuracy based on the deterministic feature can be up to 90.25%. This paper also combines the RQA features with the variation coefficient and fluctuation index, and then puts the feature vectors into a support vector machine (SVM) to automatically detect the epileptic EEG from EEG recordings. Experimental results shows that the proposed methods could achieve a great classification accuracy of 99%.