Research Topic

Seizure Forecasting and Detection: Computational Models, Machine Learning, and Translation into Devices

About this Research Topic

Seizure unpredictability is one of the most debilitating aspects of epilepsy. In recent years, advances in processing power, machine learning, and statistical modeling have accelerated a paradigm shift that has made seizure forecasting and detection a near reality. Nevertheless, studies developing new statistical and machine learning methods for seizure forecasting and detection, evaluating algorithm performance, and translation into devices and clinical decision support systems are needed. Patient perspectives and understanding of the ethics raised by these developments are needed to guide future directions of the field.

The aim of this Research Topic is to uncover novel and promising research trends in developing or applying computational models to seizure forecasting and detection and to elucidate how these algorithms can be translated into effective reduction of morbidity/mortality and meaningful improvement in patient quality of life.

This Research Topic welcomes high quality original research, perspective articles, or review articles relating to the application of current statistical or machine learning methods to seizure forecasting/detection and translation into devices, including: clinical applications, methods, new algorithm design, performance evaluation, patient perspective articles, and ethical discussion.
Potential areas of interest include, but are not limited to:
• Statistical methods and machine learning for seizure risk estimation or forecasting, using clinical or electrographic data
• Statistical methods and machine learning for automated seizure detection, using physiological or electroencephalography data
• Translation of seizure forecasting or detection into devices or clinical decision support systems
• Evaluation of sensitivity and specificity of new or established algorithms for forecasting or detection
• Impact of seizure forecasting and detection on quality of life
• Patient perspective articles
• Ethical, policy, and/or legal discussions regarding seizure forecasting or detection

Topic Editor Sharon Chiang is the co-founder of EpilepsyAI, LLC. The other Topic Editors declare no competing interests with regard to the Research Topic subject.


Keywords: Epilepsy, Seizures, Seizure Forecasting, Seizure Detection, Machine Learning, Algorithms, Ethics, Policy


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Seizure unpredictability is one of the most debilitating aspects of epilepsy. In recent years, advances in processing power, machine learning, and statistical modeling have accelerated a paradigm shift that has made seizure forecasting and detection a near reality. Nevertheless, studies developing new statistical and machine learning methods for seizure forecasting and detection, evaluating algorithm performance, and translation into devices and clinical decision support systems are needed. Patient perspectives and understanding of the ethics raised by these developments are needed to guide future directions of the field.

The aim of this Research Topic is to uncover novel and promising research trends in developing or applying computational models to seizure forecasting and detection and to elucidate how these algorithms can be translated into effective reduction of morbidity/mortality and meaningful improvement in patient quality of life.

This Research Topic welcomes high quality original research, perspective articles, or review articles relating to the application of current statistical or machine learning methods to seizure forecasting/detection and translation into devices, including: clinical applications, methods, new algorithm design, performance evaluation, patient perspective articles, and ethical discussion.
Potential areas of interest include, but are not limited to:
• Statistical methods and machine learning for seizure risk estimation or forecasting, using clinical or electrographic data
• Statistical methods and machine learning for automated seizure detection, using physiological or electroencephalography data
• Translation of seizure forecasting or detection into devices or clinical decision support systems
• Evaluation of sensitivity and specificity of new or established algorithms for forecasting or detection
• Impact of seizure forecasting and detection on quality of life
• Patient perspective articles
• Ethical, policy, and/or legal discussions regarding seizure forecasting or detection

Topic Editor Sharon Chiang is the co-founder of EpilepsyAI, LLC. The other Topic Editors declare no competing interests with regard to the Research Topic subject.


Keywords: Epilepsy, Seizures, Seizure Forecasting, Seizure Detection, Machine Learning, Algorithms, Ethics, Policy


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

01 January 2021 Abstract
01 May 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

01 January 2021 Abstract
01 May 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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