| Autor: |
Daniel John DiLorenzo, Kent W. Leyde, Dmitry Kaplan |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Brain Sciences, Vol 9, Iss 7, p 156 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3425 |
| DOI: |
10.3390/brainsci9070156 |
| Popis: |
This research study is part of a therapy development effort in which a novel approach was taken to develop an implantable electroencephalographic (EEG) based brain monitoring and seizure prediction system. Previous attempts to predict seizures by other groups had not been demonstrated to be statistically more successful than chance. The primary clinical findings from this group were published in a clinical paper; however much of the fundamental technology, including the strategy and techniques behind the development of the seizure advisory system have not been published. Development of this technology comprised several steps: a vast high quality database of EEG recordings was assembled, a structured approach to algorithm development was undertaken, an implantable 16-channel subdural neural monitoring and seizure advisory system was designed and built, preclinical studies were conducted in a canine model, and a First-In-Man study involving implantation of 15 patients followed for two years was conducted to evaluate the algorithm. The algorithm was successfully trained to correctly provide a) notification of a high likelihood of seizure in 11 of 14 patients, and b) notification of a low likelihood of seizure in 5 of 14 patients (NCT01043406). Continuous neural state monitoring shows promise for applications in seizure prediction and likelihood estimation, and insights for further research and development are drawn. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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