Zobrazeno 1 - 10
of 1 584
pro vyhledávání: '"seizure prediction"'
Publikováno v:
BMC Biomedical Engineering, Vol 6, Iss 1, Pp 1-14 (2024)
Abstract This article aims to provide and implement a patient-specific seizure (for Intervention Time (IT) detection) prediction algorithm using non-invasive data to develop warning devices to prevent further patient injury and reduce stress. Employi
Externí odkaz:
https://doaj.org/article/62190e83a0454e4880a6b4627682495c
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
Epilepsy affects 1% of the global population, with approximately one-third of patients resistant to anti-seizure medications (ASMs), posing risks of physical injuries and psychological issues. Seizure prediction algorithms aim to enhance the quality
Externí odkaz:
https://doaj.org/article/e03b632c1b97470b9d3db0ab21c32fc8
Autor:
Eryse Amira Seth, Jessica Watterson, Jue Xie, Alina Arulsamy, Hadri Hadi Md Yusof, Irma Wati Ngadimon, Ching Soong Khoo, Amudha Kadirvelu, Mohd Farooq Shaikh
Publikováno v:
Epilepsia Open, Vol 9, Iss 1, Pp 41-59 (2024)
Abstract A reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of c
Externí odkaz:
https://doaj.org/article/98b1d53aa2394d9d846bbeff07585636
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3531-3542 (2024)
Seizure prediction using EEG has significant implications for the daily monitoring and treatment of epilepsy patients. However, the task is challenging due to the underlying spatiotemporal correlations and patient heterogeneity. Traditional methods o
Externí odkaz:
https://doaj.org/article/07a7a71baec844038344a8d044a096b4
Publikováno v:
IEEE Access, Vol 12, Pp 119056-119071 (2024)
Electroencephalogram (EEG) signals are electrical signals generated by the activity between neurons in the brain and are already extensively applied for seizure prediction. Semi-supervised learning (SSL) has been applied in seizure prediction studies
Externí odkaz:
https://doaj.org/article/f085e5a8e333421091b5513fb5d6e47d
Autor:
Shuiling Shi, Wenqi Liu
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 178-188 (2024)
Seizure prediction are necessary for epileptic patients. The global spatial interactions among channels, and long-range temporal dependencies play a crucial role in seizure onset prediction. In addition, it is necessary to search for seizure predicti
Externí odkaz:
https://doaj.org/article/d77b0fc23fac4a20ba3dc575181a5beb
Autor:
Zurdo-Tabernero Marco, Canal-Alonso Ángel, de la Prieta Fernando, Rodríguez Sara, Prieto Javier, Corchado Juan Manuel
Publikováno v:
Journal of Integrative Bioinformatics, Vol 20, Iss 4, Pp 1545-602 (2023)
Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has
Externí odkaz:
https://doaj.org/article/dcc5ece4e57c4b22969c3524f21e7bb3
Publikováno v:
Frontiers in Neuroinformatics, Vol 18 (2024)
Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient’s daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the pati
Externí odkaz:
https://doaj.org/article/cc63afd10dc546f2a470ece3a66779dc
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
Externí odkaz:
https://doaj.org/article/6054b1a9ff584c4b93fb2d949b668652
Publikováno v:
Applied Sciences, Vol 14, Iss 16, p 7398 (2024)
About 70 million people globally have been diagnosed with epilepsy. Electroencephalogram (EEG) devices are the primary method for identifying and monitoring seizures. The use of EEG expands the preclinical research involving the long-term recording o
Externí odkaz:
https://doaj.org/article/ce21702f595d4665943788908a61aaf1