Single electrode EEG data of healthy and epileptic patients

Panwar, Siddharth

doi:10.5281/zenodo.3684992

Cited by 3 publications

(1 citation statement)

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“…2.1.7 Single electrode EEG data of healthy and epileptic patients [11,12] This dataset was generated with a motive to build predictive epilepsy diagnosis model and publically available since 2020. It was generated on a similar acquisition and settings i.e., sampling frequency, bandpass filtering and number of signals and time duration as of University of Bonn.…”

Section: Siena Scalp Eeg Database [9 10]mentioning

confidence: 99%

Open and free EEG datasets for epilepsy diagnosis

Handa,

Mathur,

Goel

2021

Preprint

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The Epilepsies are a common, chronic neurological disorder affecting more than 50 million individuals across the globe. It is characterized by unprovoked, recurring (similar or different type) seizures which are commonly diagnosed through clinical EEGs. Good-quality, open-access and free EEG data can act as a catalyst for on-going state-of-the-art (SOTA) research works for detection, prediction and management of epilepsy and seizures. They can also aid in improving the quality of life (QOL) of these diseased individuals and contribute research in healthcare multimedia, data analytics and Artificial Intelligence (AI) in personalized medicine. This paper presents widely used, available, open and free EEG datasets available for epilepsy and seizure diagnosis. A brief comparison and discussion of open and private datasets has also been done. Such datasets will help in development and evaluation of automatic computer-aided system in healthcare.

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Section: Siena Scalp Eeg Database [9 10]mentioning

confidence: 99%

Open and free EEG datasets for epilepsy diagnosis

Handa,

Mathur,

Goel

2021

Preprint

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Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

Handa,

Lavanya,

Goel

et al. 2024

Artif Intell Rev

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Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using routine diagnostic tests like Electroencephalography (EEG). However, manual introspection and analysis of EEG signals is presently difficult and repetitive task even for experienced neuro-technologists with high false-positive rates and inter- and intra-rater reliability. Software advancements using Artificial Intelligence (AI) algorithms have the potential to early detect and predict abnormal patterns observed in EEG signals. The present review focuses on systematically reporting software advancements and their implementation using hardware systems in automatic epilepsy diagnosis and seizure detection for the past 10 years. Traditional, hybrid, and end-to-end AI-based pipelines and associated EEG datasets have been discussed. The review summarizes and compares reported articles, datasets, and patents through various subjective and objective parameters in this field. Latest advancements demonstrate that AI-based pipelines can reduce the introspection time by at least 50% without compromising the diagnostic accuracy or abnormal event detection. A significant rise in hardware implementation of software-based pipelines, end-to-end deep learning architectures for real-time analysis, and granted patents has been noticed since 2011. More than twenty-eight datasets have been developed to automatically diagnose epileptic EEG signals from 2001 to 2023. Extensive analysis using explainability tools, cross-dataset generalizations, reproducibility analysis, and ablation experiments can further improve the existing AI-based pipelines in this field. There is a need for the development of standardized protocols for data collection and its AI pipeline for a robust, inter- and intra-rater reliability-free, and real-time automatic epilepsy diagnosis.

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EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

2023

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More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms. Researchers are working to automatically detect or predict epileptic episodes through Electroencephalography (EEG) signal analysis, and machine, and deep learning methods. Good quality, open-source, and free EEG data acts as a catalyst in this ongoing battle to manage this disease. This article presents 40+ publicly available EEG datasets for adult and pediatric human populations from 2001-2023. A comparative analysis and discussion on open and private EEG datasets have been done based on objective parameters in this domain. Bonn and CHB-MIT remain the benchmark datasets used for the automatic detection of epileptic and seizure EEG signals. Meta-data has also been released for large EEG data like CHB-MIT. This article will be updated every year to report the progress and changing trends in the development of EEG datasets in this field.

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Single electrode EEG data of healthy and epileptic patients

Cited by 3 publications

References 0 publications

Open and free EEG datasets for epilepsy diagnosis

Open and free EEG datasets for epilepsy diagnosis

Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

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