Open and free EEG datasets for epilepsy diagnosis

Handa, Palak; Mathur, Monika; Goel, Nidhi

doi:10.48550/arxiv.2108.01030

Cited by 6 publications

(7 citation statements)

References 14 publications

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“…The number of samples for seizure and non-seizure data were kept the same intentionally so that the data were balanced for machine learning. The complete description of the dataset can be found on the Mendeley data platform [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

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Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Shah

Larijani

Gibson

et al. 2022

Sensors

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Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation.

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Section: Methodsmentioning

confidence: 99%

“…In this study, a novel RNN-based deep learning model was proposed for the multi-class classification of epilepsy data. The data were obtained from a publicly available dataset [ 33 ]. Each sample in the dataset contained 19 channels, and each channel represented a data stream coming from a single electrode with a sampling rate of 500 Hz.…”

Section: Methodsmentioning

confidence: 99%

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Shah

Larijani

Gibson

et al. 2022

Sensors

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“…There are only a handful of open access data sets available for seizure detection and prediction through EEG signals (Handa et al, 2021) (Goldberger et al, 2000;Wu et al, 2019;Yan et al, 2017).…”

Section: Need Of Metadata Generation Methods For Asdsmentioning

confidence: 99%

Peri‐ictal and non‐seizure EEG event detection using generated metadata

Handa

Goel

2021

Expert Systems

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Lack of open access, seizure specific database has hindered the development of Automated Seizure Detection System (ASDS) along with state‐of‐the‐art feature selection and classification methods. Available databases contain noise, artefacts, different length & time EEG segments, associated comorbidities, clinical settings, and epilepsy/seizure types etc. Pre‐processing of such continuous EEG segments requires significant amount of time and may feed redundant information (with reference to a seizure event) to the classification model leading to inaccurate, real‐time seizure event detection systems. Hence, this paper proposes a metadata generation of large EEG databases (here, CHB‐MIT EEG scalp database v.1.0.0) for ASDS. We elucidate the need to generate seizure sensitive data through peri‐ictal and non‐seizure EEG segments. This paper performs multi‐variate analysis and two class (non‐seizure and seizure event) classification between these fixed length and time EEG segments from support vector machine (SVM) and k‐NN classifier. We thoroughly analysed the variation and dependence of different kernels, cost function, gamma, and degree for SVM based pipeline. The proposed pipeline has been compared with state‐of‐the‐art pipelines and has achieved good classification score. Such methods will help in development of generalised approach toward handling large EEG databases and machine learning applications for seizure detection and prediction.

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“…There are several publicly available databases related to epileptic seizures and EEG/iEEG signals, which have been described in works [43][44][45][46][47][48]. However, the lack of precise descriptions in these databases proved to be the biggest challenge, particularly regarding the onset and offset times of seizures and information about the electrodes on which the seizures occurred.…”

Section: Methodsmentioning

confidence: 99%

Implementation of Machine Learning and Deep Learning Techniques for the Detection of Epileptic Seizures Using Intracranial Electroencephalography

Kołodziej,

Majkowski,

Rysz

2023

Applied Sciences

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The diagnosis of epilepsy primarily relies on the visual and subjective assessment of the patient’s electroencephalographic (EEG) or intracranial electroencephalographic (iEEG) signals. Neurophysiologists, based on their experience, look for characteristic discharges such as spikes and multi-spikes. One of the main challenges in epilepsy research is developing an automated system capable of detecting epileptic seizures with high sensitivity and precision. Moreover, there is an ongoing search for universal features in iEEG signals that can be easily interpreted by neurophysiologists. This article explores the possibilities, issues, and challenges associated with utilizing artificial intelligence for seizure detection using the publicly available iEEG database. The study presents standard approaches for analyzing iEEG signals, including chaos theory, energy in different frequency bands (alpha, beta, gamma, theta, and delta), wavelet transform, empirical mode decomposition, and machine learning techniques such as support vector machines. It also discusses modern deep learning algorithms such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our goal was to gather and comprehensively compare various artificial intelligence techniques, including both traditional machine learning methods and deep learning techniques, which are most commonly used in the field of seizure detection. Detection results were tested on a separate dataset, demonstrating classification accuracy, sensitivity, precision, and specificity of seizure detection. The best results for seizure detection were obtained with features related to iEEG signal energy (accuracy of 0.97, precision of 0.96, sensitivity of 0.99, and specificity of 0.96), as well as features related to chaos, Lyapunov exponents, and fractal dimension (accuracy, precision, sensitivity, and specificity all equal to 0.95). The application of CNN and LSTM networks yielded significantly better results (CNN: Accuracy of 0.99, precision of 0.98, sensitivity of 1, and specificity of 0.99; LSTM: Accuracy of 0.98, precision of 0.96, sensitivity of 1, and specificity of 0.99). Additionally, the use of the gradient-weighted class activation mapping algorithm identified iEEG signal fragments that played a significant role in seizure detection.

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Open and free EEG datasets for epilepsy diagnosis

Cited by 6 publications

References 14 publications

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Peri‐ictal and non‐seizure EEG event detection using generated metadata

Implementation of Machine Learning and Deep Learning Techniques for the Detection of Epileptic Seizures Using Intracranial Electroencephalography

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