Opportunities for epilepsy: Outcomes of the Milken Institute State of the Science Retreat

Dumanis, Sonya B.; Riley, Ekemini A. U.; Briggs, LaTese; Kim, YooRi; Lontok, Erik; Salka, Danielle; Stevens, Melissa

doi:10.1016/j.yebeh.2016.11.017

Cited by 3 publications

(4 citation statements)

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“…Epilepsy is a central nervous system disorder characterized by recurring, spontaneous seizures (similar or different types) and hence also called seizure disorder. It is a common non-communicable disorder affecting one in twenty-six individuals worldwide (Dumanis et al, 2017). More than 12 million Persons With Epilepsy (PWE) are living in India with more than half being potential surgery candidates and at least 30%-60% undiagnosed or wrongly diagnosed as depression, anxiety disorder, migraine patients, and so forth (Amudhan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…EEG is routinely used to diagnose the type of epilepsy in a clinical setting (Dumanis et al, 2017). EEG machines are attached with appropriate hardware containing amplifiers, filters, storage devices, and software systems to view their real-time activity.…”

Section: Introductionmentioning

confidence: 99%

“…The presence of a seizure discharge is a rare activity. It may depend on the patient's behavioural event and the closeness of the EEG electrodes with the seizure onset point (Dumanis et al, 2017). Sometimes, the seizure activity may be invisible due to several factors such as the electrodes being placed far away from the epileptogenic zone, and artefact due to muscle movements.…”

Section: Introductionmentioning

confidence: 99%

“…Sometimes, the seizure activity may be invisible due to several factors such as the electrodes being placed far away from the epileptogenic zone, and artefact due to muscle movements. Hence, it is further ascertained using various multi-modalities such as computed tomography, magnetic resonance imaging scans, and so forth, to confirm the seizure localization (Dumanis et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

et al. 2023

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Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

et al. 2023

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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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Opportunities for epilepsy: Outcomes of the Milken Institute State of the Science Retreat

Cited by 3 publications

References 1 publication

A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

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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