Epileptic Seizures From Electroencephalogram Research Articles

Health Electroencephalogram epileptic classification based on Hilbert probability similarity

This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status.

Fed-ESD: Federated learning for efficient epileptic seizure detection in the fog-assisted internet of medical things

Epilepsy is a predominant paroxysmal neurological disturbance that is usually recognized as the incidence of impulsive seizures rarely seen in medicine. Automatic detection of epileptic seizures from electroencephalogram (EEG) signals is viewed as an effective diagnosis of patients on the Internet of Medical Things (IoMT). To design a robust detection service in an IoMT environment, the EEG signals of different patients are collected from geographically distributed patients to a centralized server. However, this makes the patient’s privacy prone to exposure and adds to the energy and communication costs. Also, the central server can be subject to malevolent attacks, resulting in non-efficient solutions. In this regard, for the first time, this paper presents a privacy-preserving federated learning framework for epileptic seizure detection (called Fed-ESD) from EEG signals in the fog-computing-based IoMT. A lightweight and efficient spatiotemporal transformer network is introduced to collaboratively learn spatial and temporal representations from the local data of each participant. The proposed Fed-ESD employs geographically situated fog nodes as local aggregators to enable sharing of location-based EEG signals for comparable IoMT applications. Moreover, a greedy method is introduced for deciding on the ideal fog node to be the coordinator node responsible for global aggregation during the training, thereby decreasing the reliance on the central server in the IoMT. Experimental evaluations demonstrate the efficiency of the proposed Fed-ESD in terms of detection performance, resource-efficiency, stability, and scalability for deployment in the IoMT.

Extracting epileptic features in EEGs using a dual-tree complex wavelet transform coupled with a classification algorithm

The detection of epileptic seizures from electroencephalogram (EEG) signals is traditionally performed by clinical experts through visual inspection. It is a long process, is error prone, and requires a highly trained expert. In this research, a new method is presented for seizure classification for EEG signals using a dual-tree complex wavelet transform (DT-CWT) and fast Fourier transform (FFT) coupled with a least square support vector machine (LS-SVM) classifier. In this method, each EEG signal is divided into four segments. Each segment is further split into smaller sub-segments. The DT-CWT is applied to decompose each sub-segment into detailed and approximation coefficients (real and imaginary parts). The obtained coefficients by the DT-CWT at each decomposition level are passed through an FFT to identify the relevant frequency bands. Finally, a set of effective features are extracted from the sub-segments, and are then forwarded to the LS-SVM classifier to classify epileptic EEGs. In this paper, two epileptic EEG databases from Bonn and Bern Universities are used to evaluate the extracted features using the proposed method. The experimental results demonstrate that the method obtained an average accuracy of 97.7% and 96.8% for the Bonn and Bern databases, respectively. The results prove that the proposed DT-CWT and FFT based features extraction is an effective way to extract discriminative information from brain signals. The obtained results are also compared to those by k-means and Naïve Bayes classifiers as well as with the results from the previous methods reported for classifying epileptic seizures and identifying the focal and non-focal EEG signals. The obtained results show that the proposed method outperforms the others and it is effective in detecting epileptic seziures in EEG signals. The technique can be adopted to aid neurologists to better diagnose neurological disorders and for an early seizure warning system.

Classification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (Bi-LSTM) network architecture

Epilepsy is a complex disease, difficult to detect and common among neurological diseases. The separation of epileptic and non-epileptic activity and the identification of the form of the epileptic activity on the electroencephalogram (EEG) plays an essential role in providing correct treatment to patients. At the same time, detection of epileptic seizures is very important for patients. In this study, a method to classify EEG data using deep neural network architecture is presented. In the presented method, bidirectional long-term memory (Bi-LSTM), a type of recurrent neural network (RNN) was used. Single-layer Bi-LSTM architecture memory units, neural numbers and learning algorithms were modeled. After determining the architectural hyperparameters, the single layer 10-neurons Bi-LSTM approach is proposed as the optimum model. In this model, the effect of both optimization algorithms on classification success and changing the number of neurons on performance was investigated. In the classification between epileptic activity and other activities, the success rate was 96%, success rate in binary classification 99% and the mean success rate for classification was 97.78%. Unlike previous work, this study did not just focus on success rates. In addition, the importance of determining the hyperparameters (selection of optimization algorithms, initial learning rate, number of neurons) in the Bi-LSTM model was highlighted and the effects of these choices on accuracy rates was determined. Success rate and processing load were examined in detail. This study, which emphasizes the importance of parameter optimization, provides an important perspective to this research area.

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.

An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation

Epilepsy, an incurable brain disorder portrayed by seizures, is the most common neurological disease worldwide. The embryonic detection of epileptic action helps the psychologist for the diagnosis of epileptic seizure and reduces the seizure effect on the patient’s life. Empirical wavelet transform (EWT), and a novel multi-fuse reduced deep convolutional neural network (MF-RDCNN) classifier are integrated to design a computer-aided-diagnosis (CAD) system for meticulous classification of epileptic seizure from electroencephalogram (EEG) signals. The EWT is enforced on recorded EEG signals to extract three distinct band-limited modes (BLMs) and the proposed classifier is used to compute the most informative unsupervised signatures automatically by taking the extracted BLMs as inputs. The experimental results are carried out using three EEG databases provided by Bonn-University, Germany single-channel EEG (dataset-A), Neurology and Sleep Center, New Delhi single-channel EEG (dataset-B), and Boston Children’s Hospital multichannel scalp EEG (dataset-C) to verify the effectiveness of the proposed algorithm. The performance of the proposed method is superior over other prevalent methods and has 100%,99.82% overall mean classification accuracy for two class and three class classification problems respectively using ten-fold cross-validation. Moreover, the proposed method has specificity (SPE) of 99.29%, sensitivity (SEN) of 99.86%, and classification accuracy (ACC) of 99.29% with 0.71% of false positive rate per hour (FPR/h) by taking 40% of data for training, 40% of data for testing, and the remaining 20% of data for validation from the total data incorporated with the database-C. The lesser computational complexity, higher learning speed, short event recognition time, remarkable overall mean classification accuracy, and outstanding overall performance are the major advantages of the proposed EWT-MF-RDCNN method over EWT-RDCNN and RDCNN method for accurate classification of seizure EEG epochs. The digital architecture of the MF-RDCNN classifier is also implemented in a high-speed FPGA processor to develop a CAD system for efficacious diagnosis of epileptic seizure activity online.

Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals

In this study, techniques were proposed for the detection of epileptic seizures from electroencephalogram (EEG) signals using the wavelet transform (WT), peak extraction and phase–space reconstruction (PSR) based Euclidean distances. In the first step, the wavelet coefficients were extracted after eliminating the noise from the EEG signals using a WT, which is a widely used signal processing technique. In the second step, the peaks were extracted from the wavelet coefficients. In the third step, the continuous peaks that were extracted were mapped to 3D coordinates using PSR. In the fourth step, the Euclidean distances between the mapped 3D coordinates and the origin were obtained. The features of the Euclidean distances obtained were extracted using statistical techniques. The final features extracted were used as inputs to the neural network with weighted fuzzy membership (NEWFM). NEWFM contains the bounded sum of weighted fuzzy memberships (BSWFMs) that can reveal the differences in the graphic characteristics between normal EEG signals and epileptic-seizure EEG signals. The BSWFMs can easily be embedded in a portable device to detect epileptic seizures from EEG signals in real life.

A Survey on Epilepsy Seizure Detection Using Machine Learning Technique

Epilepsy, One of the most prevalent neurological disorder. Its a chronic condition is characterized by voluntary, unpredictable, and recurrent seizures that affects millions of individuals worldwide. A brief alteration in normal brain function that affects the health of patients occurs in this chronic condition. Detection of epileptic seizures before the start of the onset is beneficial. Recent studies have suggested approaches to machine learning that automatically execute those diagnostic tasks by integrating statistics and computer science. Machine learning, an application of AI (Artificial Intelligence) technology, allows a machine to learn something new automatically and thereby improve its output through meaningful data. For the prediction of epileptic seizures from electroencephalogram (EEG) signals, machine learning techniques and computational methods are used. There is a vast amount of medical data available today about the disease, its symptoms, causes of illness and its effects. But this data is not analyzed properly to predict or to study a disease. The objective of this paper is to provide detailed versions of machine learning predictive models for predicting epilepsy seizure detection and describing several types of predictive models and their applications in the field of healthcare. So that seizures can be predicted earlier before it occurs, it will be useful for epilepsy patients to improve their safety and quality of their life.

A Survey on Epilepsy Seizure Detection Using Machine Learning Technique

Epilepsy, One of the most prevalent neurological disorder. Its a chronic condition is characterized by voluntary, unpredictable, and recurrent seizures that affects millions of individuals worldwide. A brief alteration in normal brain function that affects the health of patients occurs in this chronic condition. Detection of epileptic seizures before the start of the onset is beneficial. Recent studies have suggested approaches to machine learning that automatically execute those diagnostic tasks by integrating statistics and computer science. Machine learning, an application of AI (Artificial Intelligence) technology, allows a machine to learn something new automatically and thereby improve its output through meaningful data. For the prediction of epileptic seizures from electroencephalogram (EEG) signals, machine learning techniques and computational methods are used. There is a vast amount of medical data available today about the disease, its symptoms, causes of illness and its effects. But this data is not analyzed properly to predict or to study a disease. The objective of this paper is to provide detailed versions of machine learning predictive models for predicting epilepsy seizure detection and describing several types of predictive models and their applications in the field of healthcare. So that seizures can be predicted earlier before it occurs, it will be useful for epilepsy patients to improve their safety and quality of their life.

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD.

We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels (e.g., an accuracy of 0.93 using five channels). We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects, after reducing the number of instances, based on the k-means algorithm.

Epileptic seizures identification with autoregressive model and firefly optimization based classification

Identifying epilepsy cases and epileptic seizures from electroencephalogram (EEG) signals is a challenging issue, which usually needs high level of skilled neurophysiologists. Numerous works have attempted to develop tools that can provide an assistant to neurophysiologist in analyzing the EEG for epileptic seizures detection. This paper proposes a new automatic framework to identify and classify the epileptic seizure from EEG using a machine learning method. In particular, the feature extraction process of the proposed scheme utilizes autoregressive model (AR) and firefly optimization (FA) to procure an optimal model order (P). Namely, the main aim of FA is to find the best model order (P) with minimum residual variance using Akaike information criterion (AIC) as an objective function of FA algorithm. A support vector machine (SVM) classifier is employed for the classification of the epileptic seizures signals. The presented scheme is also effective for short segment of EEG signals owing to use of AR model in features extraction stage. Experiments with the publicly available Bonn database that is composed of healthy (nonepileptic), interictal and ictal EEG samples show promising results with high accuracy.

An efficient error-minimized random vector functional link network for epileptic seizure classification using VMD

In this paper, variational mode decomposition (VMD), Hilbert transform (HT), and proposed error-minimized random vector functional link network (EMRVFLN) are integrated to detect and classify epileptic seizure from electroencephalogram (EEG) signals. VMD is applied to decompose the EEG signal into Band-limited intrinsic mode functions (BLIMFs). The five efficacious instantaneous features are computed using HT to construct the feature vector. Proposed EMRVFLN classifier is used to classify the epileptic seizure. The performances of the proposed EMRVFLN are compared with recently developed classifiers such as least-square support vector machine (LSSVM) and extreme learning machine (ELM). The combination of VMD and HT with proposed EMRVFLN classifier outperforms other state-of-the-art methods with classification accuracy of 100% for two class classification problem and 99.74% for three class classification problem. The remarkable classification accuracy facilitates the digital implementation of the proposed EMRVFLN classifier which may aid to design an embedded system for real-time disease diagnosis.

Epileptic seizures detection in EEGs blending frequency domain with information gain technique

This paper proposes a new algorithm which combines the information in frequency domain with the Information Gain (InfoGain) technique for the detection of epileptic seizures from electroencephalogram (EEG) data. The proposed method consists of four main steps. Firstly, in order to investigate which method is most suitable to decompose the EEG signals into frequency bands, we implement separately a fast Fourier transform (FFT) or discrete wavelet transform (DWT). Secondly, each band is partitioned into k windows and a set of statistical features are extracted from each window. Thirdly, the InfoGain is used to rank the extracted features and the most important ones are selected. Lastly, these features are forwarded to a least square support vector machine (LS-SVM) classifier to classify the EEG. This scheme is implemented and tested on a benchmark EEG database and also compared with other existing methods, based on some performance evaluation measures. The experimental results show that the proposed FFT combined with InfoGain method can generate better performance than the DWT method. This method achieves 100% accuracy for five different pairs: healthy people with eyes open (z) versus epileptic patients with activity seizures (s); healthy people with eyes closed (o) versus s; epileptic patients with free seizures (n) versus s; patients with free seizures epileptic (f) versus s; and z versus o. The accuracies obtained for two other pairs, (o vs. n) and (z vs. f), are 95.62 and 88.32%, respectively. These two pairs have more similarities with each other, leading to a lower level of accuracy. The proposed approach outperforms six other reported methods and achieves an 11.9% improvement. Finally, it can be concluded that the proposed FFT combined with InfoGain method has the capacity to detect epileptic seizures in EEG most effectively.

Effect of filtering with time domain features for the detection of epileptic seizure from EEG signals

Pattern recognition plays an important role in the detection of epileptic seizure from electroencephalogram (EEG) signals. In this pattern recognition study, the effect of filtering with the time domain (TD) features in the detection of epileptic signal has been studied using naive Bayes (NB) and supports vector machines (SVM). It is the first time the authors attempted to use TD features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived from the filtered and unfiltered EEG data, and performance of these features is studied along with mean absolute value (MAV) which has been already attempted by the researchers. The other TD features which are attempted by the researchers such as standard deviation (SD) and average power (AVP) along with MAV are studied. A comparison is made in effect of filtering and without filtering for the University of Bonn database using NB and SVM for the TD features attempted first time along with MAV. The effect of individual and combined TD features is studied and the highest classification accuracy obtained in using direct TD features would be 99.87%, whereas it is 100% with filtered EEG data. The raw EEG data can be segmented and filtered using the fourth-order Butterworth band-pass filter.

Epileptic seizure detection from EEG signals with phase–amplitude cross-frequency coupling and support vector machine

As a pattern of cross-frequency coupling (CFC), phase–amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB–MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6[Formula: see text]s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

A computer aided analysis scheme for detecting epileptic seizure from EEG data

This paper presents a computer aided analysis system for detecting epileptic seizure from electroencephalogram (EEG) signal data. As EEG recordings contain a vast amount of data, which is heterogeneous with respect to a time-period, we intend to introduce a clustering technique to discover different groups of data according to similarities or dissimilarities among the patterns. In the proposed methodology, we use K-means clustering for partitioning each category EEG data set (e.g. healthy; epileptic seizure) into several clusters and then extract some representative characteristics from each cluster. Subsequently, we integrate all the features from all the clusters in one feature set and then evaluate that feature set by three well-known machine learning methods: Support Vector Machine (SVM), Naive bayes and Logistic regression. The proposed method is tested by a publicly available benchmark database: ‘Epileptic EEG database’. The experimental results show that the proposed scheme with SVM classifier yields overall accuracy of 100% for classifying healthy vs epileptic seizure signals and outperforms all the recent reported existing methods in the literature. The major finding of this research is that the proposed K-means clustering based approach has an ability to efficiently handle EEG data for the detection of epileptic seizure.

Application of MODWT and log-normal distribution model for automatic epilepsy identification

In this paper, a novel approach based on the maximal overlap discrete wavelet transform (MODWT) and log-normal distribution (LND) model was proposed for identifying epileptic seizures from electroencephalogram (EEG) signals. To carry out this study, we explored the potentials of MODWT to decompose the signals into time-frequency sub-bands till sixth level. And demodulation analysis (DA) was investigated to reveal the subtle envelope information from the sub-bands. All obtained coefficients were then used to calculate LDN features, scale parameter (σ) and shape parameter (μ), which were fed to a random forest classifier (RFC) for classification. Besides, some experiments have been conducted to evaluate the performance of proposed model in the consideration of various wavelet functions as well as feature extractors. The implementation results demonstrated that our proposed technique has yielded remarkable classification performance for all the concerned problems and outperformed the reported methods in terms of the universality. The major finding of this research is that the proposed technique was capable of classifying EEG segments with satisfied accuracy and clinically acceptable computational time. These advantages have make our technique an attractive diagnostic and monitoring tool, which helps doctors in providing better and timely care for the patients.

Optimal feature selection using PSO with SVM for epileptic EEG classification

Electroencephalogram (EEG) signals usually have a high dimensionality which makes it difficult for classifiers to learn the difference of various classes in the underlying pattern in the signal. Feature selection is one of the most important factors which can influence the classification accuracy rate. The objective of this research is to simultaneously optimise the parameters and feature subset. A novel scheme to detect epileptic seizures from electroencephalogram (EEG) is proposed in this paper. This scheme is based on discrete wavelet packet transform and uses the transform coefficients to compute energy, entropy, kurtosis, skewness, mean, median and standard deviation to form feature vector for classification. Optimal features are selected and parameters are optimised using Particle Swarm Optimisation (PSO) with support vector machine as a classifier for creating objective function values for the PSO. Clinical EEG data from epileptic and normal subjects are used in the experiment. To evaluate the efficacy of the proposed scheme, a tenfold cross-validation is implemented, and the detection rate is found 100% accurate with 100% of sensitivity and specificity for the data under consideration.

Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization

In this study, we offered a new feature extraction approach called probability distribution based on equal frequency discretization (EFD) to be used in the detection of epileptic seizure from electroencephalogram (EEG) signals. Here, after EEG signals were discretized by using EFD method, the probability densities of the signals were computed according to the number of data points in each interval. Two different probability density functions were defined by means of the polynomial curve fitting for the subjects without epileptic seizure and the subjects with epileptic seizure, and then when using the mean square error criterion for these two functions, the success of epileptic seizure detection was 96.72%. In addition, when the probability densities of EEG segments were used as the inputs of a multilayer perceptron neural network (MLPNN) model, the success of epileptic seizure detection was 99.23%. This results show that non-linear classifiers can easily detect the epileptic seizures from EEG signals by means of probability distribution based on EFD.

Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy

In this study, a new scheme was presented for detecting epileptic seizures from electro-encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new scheme was based on approximate entropy (ApEn) and discrete wavelet transform (DWT) analysis of EEG signals. Seizure detection was accomplished in two stages. In the first stage, EEG signals were decomposed into approximation and detail coefficients using DWT. In the second stage, ApEn values of the approximation and detail coefficients were computed. Significant differences were found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with over 96% accuracy. Without DWT as preprocessing step, it was shown that the detection rate was reduced to 73%. The analysis results depicted that during seizure activity EEG had lower ApEn values compared to normal EEG. This suggested that epileptic EEG was more predictable or less complex than the normal EEG. The data was further analyzed with surrogate data analysis methods to test for evidence of nonlinearities. It was shown that epileptic EEG had significant nonlinearity whereas normal EEG behaved similar to Gaussian linear stochastic process.