The DOI matches its Crossref record; 149 of the 151 references it cites were found in a registry or an index.
Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy
Open accessAbstract
Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.
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“There is also a growing interest in using wearable devices to detect seizures in epilepsy [2].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [2]
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A review of epileptic seizure detection using machine learning classifiers
Siddiqui, M. K., Morales-Menéndez, R., Huang, X., & Hussain, N. (2020) · Brain Informatics · doi:10.1186/s40708-020-00105-1 · Cited by 488
Metadata
- Authors
- Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N. E., Sánchez Fernández, I., Klehm, J., Bosl, W., Reinsberger, C., Schachter, S., & Loddenkemper, T.
- Affiliations
- Boston Children's Hospital (US)
- University of San Francisco (US)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Corresponding author
- Loddenkemper, Tobias
- ORCID
- Venue
- Epilepsy & Behavior
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 37
- Pages
- 291-307
- ISSN
- 1525-5050 (ISSN-L)
- OpenAlex
- W2133082272
- Cited by
- 469
- References
- 151
- Citations by year
- 2026: 18 · 2025: 19 · 2024: 30 · 2023: 17 · 2022: 25 · 2021: 55 · 2020: 53 · 2019: 52 · 2018: 72 · 2017: 50 · 2016: 43 · 2015: 32 · 2014: 2
- FWCI
- 23.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Epilepsy & Behavior (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- USF Scholarship Repository (University of San Francisco) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Clinical Neurology › Epilepsy research and treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Seismology and Earthquake Studies
- Keywords
- epilepsy, seizure detection, seizure prediction, automated seizure detection, intracranial EEG, scalp EEG, accelerometry, electrodermal activity, SUDEP prevention, accident prevention, feature extraction, machine learning classifiers
- MeSH terms
- Adolescent, Algorithms, Child, Child, Preschool, Electrocardiography, Electroencephalography, Epilepsy, Humans, Markov Chains, Motion, Predictive Value of Tests, Scalp, Seizures, Sensitivity and Specificity
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