Seizure Susceptibility Prediction in Uncontrolled Epilepsy
Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in additi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Neurology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2021.721491/full |
| _version_ | 1818643935100141568 |
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| author | Nhan Duy Truong Nhan Duy Truong Yikai Yang Christina Maher Levin Kuhlmann Levin Kuhlmann Alistair McEwan Armin Nikpour Armin Nikpour Omid Kavehei Omid Kavehei |
| author_facet | Nhan Duy Truong Nhan Duy Truong Yikai Yang Christina Maher Levin Kuhlmann Levin Kuhlmann Alistair McEwan Armin Nikpour Armin Nikpour Omid Kavehei Omid Kavehei |
| author_sort | Nhan Duy Truong |
| collection | DOAJ |
| description | Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of “What are the true biomarkers for seizure prediction” or even “Is there a valid biomarker for seizure prediction?” are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction. |
| first_indexed | 2024-12-17T00:06:51Z |
| format | Article |
| id | doaj.art-58cee05f42fa4270889a50b9265d98a2 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-12-17T00:06:51Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-58cee05f42fa4270889a50b9265d98a22022-12-21T22:10:56ZengFrontiers Media S.A.Frontiers in Neurology1664-22952021-09-011210.3389/fneur.2021.721491721491Seizure Susceptibility Prediction in Uncontrolled EpilepsyNhan Duy Truong0Nhan Duy Truong1Yikai Yang2Christina Maher3Levin Kuhlmann4Levin Kuhlmann5Alistair McEwan6Armin Nikpour7Armin Nikpour8Omid Kavehei9Omid Kavehei10Australian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, AustraliaThe University of Sydney Nano Institute, Sydney, NSW, AustraliaAustralian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, AustraliaAustralian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, AustraliaFaculty of Information Technology, Monash University, Melbourne, VIC, AustraliaDepartment of Medicine - St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, AustraliaAustralian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, AustraliaComprehensive Epilepsy Service and Department of Neurology at the Royal Prince Alfred Hospital, Sydney, NSW, AustraliaFaculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, NSW, AustraliaAustralian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, AustraliaThe University of Sydney Nano Institute, Sydney, NSW, AustraliaEpileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of “What are the true biomarkers for seizure prediction” or even “Is there a valid biomarker for seizure prediction?” are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.https://www.frontiersin.org/articles/10.3389/fneur.2021.721491/fullepileptic seizure forecastingprobabilistic programmingBayesianvariational inferenceuncertainty level |
| spellingShingle | Nhan Duy Truong Nhan Duy Truong Yikai Yang Christina Maher Levin Kuhlmann Levin Kuhlmann Alistair McEwan Armin Nikpour Armin Nikpour Omid Kavehei Omid Kavehei Seizure Susceptibility Prediction in Uncontrolled Epilepsy Frontiers in Neurology epileptic seizure forecasting probabilistic programming Bayesian variational inference uncertainty level |
| title | Seizure Susceptibility Prediction in Uncontrolled Epilepsy |
| title_full | Seizure Susceptibility Prediction in Uncontrolled Epilepsy |
| title_fullStr | Seizure Susceptibility Prediction in Uncontrolled Epilepsy |
| title_full_unstemmed | Seizure Susceptibility Prediction in Uncontrolled Epilepsy |
| title_short | Seizure Susceptibility Prediction in Uncontrolled Epilepsy |
| title_sort | seizure susceptibility prediction in uncontrolled epilepsy |
| topic | epileptic seizure forecasting probabilistic programming Bayesian variational inference uncertainty level |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2021.721491/full |
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