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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]

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]

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]

• 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]