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This work uses Fuzzy Lattices and Neural Reinforcement Learning techniques for seizure classification. EEG database of Bonn University and CHB-MIT has been used for the evaluation of the proposed method. Here, features are represented in the form of fuzzy lattices, and feature vectors are created in the form of Kinetic Energy (K.E.) using Schrödinger equation. Then, the highest K.E.-based seven fuzzy lattices have been used for classification using Neural Reinforcement Learning classifier. Neural Reinforcement Learning classifier is a self-learner method that classifies different seizure sub-classes (healthy eyes open, healthy eye closed, Epileptogenic Zone, hippocampal formation of opposite hemisphere, epileptic seizure). The effectiveness of the proposed method has been tested on two different public datasets. The average classification accuracy achieved is 97.6% and 97.5% for Bonn and CHB-MIT datasets, respectively. Results are compared with existing techniques to show the precedence of proposed approach. Also, computation speed of proposed classifier is more than 1.5 times compared to Fuzzy Q Learning classifier. The objective is to develop a hybrid model integrating fuzzy lattices and neural reinforcement learning (adaptive methods) for accurate classification, aiming to enhance and speeding up seizure detection and diagnosis to improve patient care through advanced computational techniques. [ABSTRACT FROM AUTHOR]

Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88–100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold crossvalidation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Background: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. Method: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. Results: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. Conclusion: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts and automate many ML processes to create a high-performing ML model. This article explores the use of automated machine learning (AutoML) tools for diagnosing epilepsy using electroencephalogram (EEG) data. The study compares the performance of three different AutoML tools, AutoGluon, Auto-Sklearn, and Amazon Sagemaker, on three different datasets from the UC Irvine ML Repository, Bonn EEG time series dataset, and Zenodo. Performance measures used for evaluation include accuracy, F1 score, recall, and precision. The results show that all three AutoML tools were able to generate high-performing ML models for the diagnosis of epilepsy. The generated ML models perform better when the training dataset is larger in size. Amazon Sagemaker and Auto-Sklearn performed better with smaller datasets. This is the first study to compare several AutoML tools and shows that AutoML tools can be utilized to create well-performing solutions for the diagnosis of epilepsy via processing hard-to-analyze EEG timeseries data. [ABSTRACT FROM AUTHOR]

Introduction: Intelligent recognition of electroencephalogram (EEG) signals can remarkably improve the accuracy of epileptic seizure prediction, which is essential for epileptic diagnosis. Extreme learning machine (ELM) has been applied to EEG signals recognition, however, the artifacts and noises in EEG signals have a serious effect on recognition efficiency. Deep learning is capable of noise resistance, contributing to removing the noise in raw EEG signals. But traditional deep networks suffer from time-consuming training and slow convergence. Methods: Therefore, a novel deep learning based ELM (denoted as DELM) motivated by stacking generalization principle is proposed in this paper. Deep extreme learning machine (DELM) is a hierarchical network composed of several independent ELM modules. Augmented EEG knowledge is taken as complementary component, which will then be mapped into next module. This learning process is so simple and fast, meanwhile, it can excavate the implicit knowledge in raw data to a greater extent. Additionally, the proposed method is operated in a single-direction manner, so there is no need to perform parameters fine-tuning, which saves the expense of time. Results: Extensive experiments are conducted on the public Bonn EEG dataset. The experimental results demonstrate that compared with the commonly-used seizure prediction methods, the proposed DELM wins the best average accuracies in 13 out of the 22 data and the best average F-measure scores in 10 out of the 22 data. And the running time of DELM is more than two times quickly than deep learning methods. Discussion: Therefore, DELM is superior to traditional and some state-of- the-art machine learning methods. The proposed architecture demonstrates its feasibility and superiority in epileptic EEG signal recognition. The proposed less computationally intensive deep classifier enables faster seizure onset detection, which is showing great potential on the application of real-time EEG signal classification. [ABSTRACT FROM AUTHOR]

Temporal lobe epilepsy, a neurological disease that causes seizures as a result of excessive neural activities in the brain, is the most common type of focal seizure, accounting for 30–35% of all epilepsies. Detection of epilepsy and localization of epileptic focus are essential for treatment planning and epilepsy surgery. Currently, epileptic focus is decided by expert physician by examining the EEG records and determining EEG channel where epileptic patterns begins and continues intensely during seizure. Examination of long EEG recordings is very time-consuming process, requires attention and decision can vary depending on physician. In this study, to assist physicians in detecting epileptic focus side from EEG recordings, a novel deep learning-based computer-aided diagnosis system is presented. In the proposed framework, ictal epochs are detected using long short-term memory network fed with EEG subband features obtained by discrete wavelet transform, and then, epileptic focus identification is realized by using asymmetry score. This algorithm was tested on EEG database obtained from the Ankara University hospital. Experimental results showed ictal and interictal epochs were classified with accuracy of 86.84%, sensitivity of 86.96% and specificity of 89.68% on Ankara University hospital dataset, and 96.67% success rate was obtained on Bonn EEG dataset. In addition, epileptic focus was identified with accuracy of 96.10%, sensitivity of 100% and specificity of 93.80% by using the proposed deep learning-based algorithm and university hospital dataset. These results showed that proposed method can be used properly in clinical applications, epilepsy treatment and surgical planning as a medical decision support system. [ABSTRACT FROM AUTHOR]

The detection of epileptic seizures is key for neurologists to initiate the right treatment at the earliest. However, the traditional methods are dependent on manual diagnosis which are error-prone. Hence, the current article presents an automated machine learning-based approach with competing results to detect seizures and its types using traditional features. Towards this goal, the present paper explores the performance of two fractal parameters namely Higuchi and, Kat'z fractal dimension, along with power spectral density and spectral entropy to detect EEG signals with seizure and non-seizure characteristics. To validate the performance via cross-validation technique, the features are extracted and experimented from three publically available databases such as Bern-Barcelona (focal, non-focal), Khas (preictal, interictal, ictal), and Bonn. Thereafter evaluated with different machine learning algorithms like Random Forest classifier, Ada Boost classifier, Gradient Boosting classifier, Extra Tree classifier, SVM classifier, and Naive Bayes classifier. The experiments showed 100% F1 score and accuracy in classifying focal and non-focal signals for random forest and extra tree classifier methods. To the best of our knowledge, this is the first paper reporting this score for the Bern-Barcelona dataset. The same features are also experimented for the detection of interictal, ictal and preictal scenarios. In the process, 100% accuracy is obtained in classifying interictal and ictal EEG signals, and an accuracy of 94% is attained in classifying interictal-preictal signals. This score is 14% higher than the current state-of-the-art methods using the same database. The features are also used in the Bonn database, where the proposed approach gave an accuracy of 100% same as the state-of-the-art methods. • Obtained competing evaluation score relative to SOTA methods using ML methods. • Competing score reported for interictal-preictal based classification of EEG data. • EEG classification is performed in the sensing time domain itself. • The proposed approach is validated using three benchmark datasets. • The performance is evaluated using cross-validation technique. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder of the central nervous system, characterized by sudden seizures caused by abnormal electrical discharges in the brain. Electroencephalogram (EEG) is the most common technique used for Epilepsy diagnosis. Generally, it is done by the manual inspection of the EEG recordings of active seizure periods (ictal). Several techniques have been proposed throughout the years to automate this process. In this study, we have developed three different approaches to extract features from the filtered EEG signals. The first approach was to extract eight statistical features directly from the time-domain signal. In the second approach, we have used only the frequency domain information by applying the Discrete Cosine Transform (DCT) to the EEG signals then extracting two statistical features from the lower coefficients. In the last approach, we have used a tool that combines both time and frequency domain information, which is the Discrete Wavelet Transform (DWT). Six different wavelet families have been tested with their different orders resulting in 37 wavelets. The first three decomposition levels were tested with every wavelet. Instead of feeding the coefficients directly to the classifier, we summarized them in 16 statistical features. The extracted features are then fed to three different classifiers k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to perform two binary classification scenarios: healthy versus epileptic (mainly from interictal activity), and seizure-free versus ictal. We have used a benchmark database, the Bonn database, which consists of five different sets. In the first scenario, we have taken six different combinations of the available data. While in the second scenario, we have taken five combinations. For Epilepsy detection (healthy vs epileptic), the first approach performed badly. Using the DCT improved the results, but the best accuracies were obtained with the DWT-based approach. For seizure detection, the three methods performed quite well. However, the third method had the best performance and was better than many state-of-the-art methods in terms of accuracy. After carrying out the experiments on the whole EEG signal, we separated the five rhythms and applied the DWT on them with the Daubechies7 (db7) wavelet for feature extraction. We have observed that close accuracies to those recorded before can be achieved with only the Delta rhythm in the first scenario (Epilepsy detection) and the Beta rhythm in the second scenario (seizure detection). [ABSTRACT FROM AUTHOR]

Epilepsy is a psychosocial neurological disorder, which emerges as a major threat to public health. In this age of the internet of things, the smart diagnosis of epilepsy has gained huge research attention with machine learning-based seizure detection in cloud-fog assisted environments. The present paper also proposes a cloud-fog integrated smart neurocare approach, which performs a temporal analysis of raw electroencephalogram (EEG) signals using deep learning to detect the occurrence of epileptic seizures. This patient-independent approach makes use of single-channel EEG signals to achieve real-time and computationally efficient seizure detection at fog layer devices. It employs a maximum variance-based channel selection procedure to select only one channel of raw scalp EEG signals, followed by their filtering and segmentation into various short-duration temporal segments. To analyse EEG patterns, these segments are further fed to the proposed models of convolutional neural network, recurrent neural network and stacked autoencoder deep learning classifiers. The performance analysis through simulation results evidently reveals that the proposed convolutional neural network-based temporal analysis approach performs better than other approaches. It realises an optimum accuracy of 96.43%, sensitivity of 100% and specificity of 93.33% for 30s duration EEG segments of CHB-MIT dataset and achieves 100% accuracy, sensitivity and specificity values for EEG segments of Bonn dataset for 23.6s EEG segments. Thus, the proposed convolutional neural network-based approach emerges as an appropriate method for rapid and accurate detection of epileptic seizures in fog-cloud integrated environment. [ABSTRACT FROM AUTHOR]

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task. [ABSTRACT FROM AUTHOR]

Neural network (NN) finds role in variety of applications due to combined effect of feature extraction and classification availability in deep learning algorithms. In this paper, we have chosen SVM, logistic regression machine learning algorithms and NN for EEG signal classification. Two-layer LSTM and four-layer improved NN deep learning algorithms are proposed to improve the performance in EEG classification. Novelty lies in one-dimensional gradient descent activation functions with radial basis operations used in the initial layers of improved NN which help in achieving better performance. Statistical features namely mean, standard deviation, kurtosis and skewness are extracted for input EEG collected from Bonn database and then applied for various classification techniques. Accuracy, precision, recall and F1 score are the performance metrics used for analyzing the algorithms. Improved NN and LSTM give better performance compared to all other architectures. The simulations are carried out with variety of activation functions, optimizers and loss models to analyze the performance using Python in keras. [ABSTRACT FROM AUTHOR]

• Early diagnosis, including the combination of high accuracy and low computational complexity, is important in real applications of computer-aided diagnosis systems. • We present an approach to seizure detection from electroencephalogram (EEG) signals with complexity analysis and comparison. • The methodology was tested on the short-term Bonn EEG dataset and the long-term CHB-MIT EEG dataset. The results lead to the conviction that the designed solution represents a suitable tool. • The proposed solution should be implemented in all clinical or home environments for decision support. Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support. [ABSTRACT FROM AUTHOR]

• The SSTFT is used to map the EEG into time–frequency domain. • We introduce GNMF to make a quadratic interpretation of TFR. • The FKNN is then adopted to recognize the EEG patterns based on the LogE features. • The obtained results confirm the transfer ability of the proposed features. 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. [ABSTRACT FROM AUTHOR]

• An optimized machine learning model, Support Vector Machine (SVM) for the diagnosis of epilepsy seizure is proposed. • A novel Hybrid Grey Wolf Optimizer-Improved Sine Cosine Algorithm (HGWOISCA) was proposed to select the salient features. • The HGWOISCAA-SVM –epilepsy diagnosis performance was compared to the performance of earlier methods. • The proposed model achieved classification accuracy of 100% for Bonn university data and 99.9% for clinical data. Analysis and detection of epileptic seizures are usually done by physicians through the visual scanning of EEG signals, which tends to be subjective, time-consuming, inaccurate, and prone to errors. Various research directions have focused on the detection and classification of epilepsy using machine learning methods. However, the prevailing methods have shortcomings like low classification rates and slow convergence. To address those limitations, this paper designs a fully automated system based on Hybrid Grey Wolf Optimizer-Improved Sine Cosine Algorithm (HGWOISCA) enhanced Support Vector Machine (SVM) named HGWOISCA-SVM for EEG signal classification. Primarily, unprocessed EEG signals are denoised by using an improved wavelet thresholding function to diminish noise and redundant data. Secondarily, three types of features such as wavelet domain features, time-domain features, and chaotic features are computed from the preprocessed EEG signals. In the following phase, Enhanced Grasshopper Optimization Algorithm (EGOA) is adopted to select optimal features with high discriminative power and to reduce dimensionality. Finally, the selected features are input into the HGWOISCA-SVM to differentiate healthy EEG signals from epileptic seizure signals. The effectiveness of the developed system is investigated by using Bonn university data and clinical data. Experiential results prove that the developed system yields a high classification rate of 100% for Bonn university data and 99.9% for clinical data, proving to be a powerful system for epilepsy classification. A comparison of the proposed system with the existing epileptic seizure detection methods is also done for validation. [ABSTRACT FROM AUTHOR]

• Automatic detection of epileptic seizures in EEG signals using new CADS based on artificial intelligence techniques. • The combining of fuzzy entropies for features extraction. • Autoencoder with proposed layers is used for dimension reduction of feature matrix. • The ANFIS classifier with BS optimizer is used for classification. Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74% in classifying into two classes and an accuracy of 99.46% in ternary classification on the Bonn dataset and 99.28% on the Freiburg dataset, reaching state-of-the-art performances on both of them. [ABSTRACT FROM AUTHOR]

Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively. [ABSTRACT FROM AUTHOR]

• Synchroextracting chirplet transform is exploited for epileptic EEG signal representation. • The accurate TF trajectory obtained by SECO can fully explore the effective information of the epilepsy signal. • Only the TF coefficients most relevant to the IF trajectory are extracted to remove the irrelevant noise interference. • Satisfied performance indicators are both obtained using different EEG datasets including Bonn and CHB-MIT. • The strong generalization ability and portability of the proposed method guarantee the application in clinical. With the development of computer technology, computer-aided medical diagnosis based on signal processing is trending up. In recent years, the detection of epilepsy electroencephalogram (EEG) signals for clinical application has attracted extensive attention from many researchers. In this paper, a novel epilepsy classification model is proposed based on the time-frequency (TF) analysis method named synchroextracting chirplet transform (SECT). The energy concentrated TF representation of the signal is first obtained benefit by the introduced parameter chirp rate. Then the instantaneous frequency (IF) trajectory is estimated by extracting the TF coefficients most relevant to the signal through the synchroextracting chirplet operator (SECO). The original signal can be simple and effectively reconstructed using the accurate IF trajectory with anti-noise interference ability. Singular value decomposition is performed on the TF matrix to obtain the feature vectors with strong characterization capacity for different epilepsy behaviors, and the multiple classification tasks are finally completed with support vector machine (SVM) classifier. Two authoritative epilepsy datasets are employed in our work to verify the effectiveness of the proposed scheme. No less than 99.17 % accuracy, 99.33 % specificity, and 99.28 % sensitivity are achieved on a total of ten classification tasks that are closely related to the actual clinical applications in the Bonn dataset; an average of 99.29 % accuracy and 0.97 matthews correlation coefficient (MCC) are obtained among all subjects in the long-term EEG database CHB-MIT. The noise robustness and portability of our proposal are verified through the stable experimental results. The superior detection accuracy also provides clinicians with auxiliary diagnosis. [ABSTRACT FROM AUTHOR]

• The seizures are the predominant symptom of epilepsy. • The third order cumulant (ToC) is used to initially extract the features EEG signals. • The sparse autoencoder is used to extract the structural information from the ToC features. • The sparse autoencoder based neural network gives up to 100% accuracy. The epileptic seizure is a transient and abnormal discharge of nerve cells in the brain that leads to a chronic disease of brain dysfunction. There are various features-based seizures classification algorithms listed in the literature. But, there is no standardized set of attributes that can perfectly capture the relevant information regarding the signal dynamics. In this paper, a computationally-fast seizure classification algorithm is presented. The obtained results through the proposed algorithm are consistent and repeatable. This paper describes an automated seizures classification technique using the nonlinear higher-order statistics and deep neural network algorithms. The sparse autoencoder based deep neural network is used to extract the essential structural details from the third-order cumulant coefficients matrix. The proposed algorithm achieves a reliable classification accuracy for both categories, i.e., binary classes and three-classes of electroencephalogram (EEG) signals with the softmax classifier. The proposed study is simulated on the publicly-available Bonn university EEG database. The achieved results show the effectiveness of the proposed algorithm for seizures classification. [ABSTRACT FROM AUTHOR]

Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in clinical, which provides valuable information for understanding the physiological mechanisms behind epileptic disorders. Approximate entropy and recurrence quantification analysis are nonlinear analysis tools to quantify the complexity and recurrence behaviors of non-stationary signals, respectively. Convolutional neural networks are powerful class of models. In this paper, a new method for automatic epileptic electroencephalogram recordings based on the approximate entropy and recurrence quantification analysis combined with a convolutional neural network were proposed. The Bonn dataset was used to assess the proposed approach. The results indicated that the performance of the epileptic seizure detection by approximate entropy and recurrence quantification analysis is good (all of the sensitivities, specificities and accuracies are greater than 80%); especially the sensitivity, specificity and accuracy of the recurrence rate achieved 92.17%, 91.75% and 92.00%. When combines the approximate entropy and recurrence quantification analysis features with convolutional neural networks to automatically differentiate seizure electroencephalogram from normal recordings, the classification result can reach to 98.84%, 99.35% and 99.26%. Thus, this makes automatic detection of epileptic recordings become possible and it would be a valuable tool for the clinical diagnosis and treatment of epilepsy. [ABSTRACT FROM AUTHOR]