Comparison of DL methods using QEEG.

<div><p>Strategies to predict neonatal seizure risk have typically focused on long-term static predictions with prediction horizons spanning days during the acute postnatal period. Higher temporal resolution or short-horizon neonatal seizure prediction, on the time-frame of minutes, rema...

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主要作者:	Jonathan Kim (2601136) (author)
其他作者:	Edilberto Amorim (12368915) (author), Vikram R. Rao (21702554) (author), Hannah C. Glass (21702557) (author), Danilo Bernardo (21702560) (author)
出版:	2025
主題:	Microbiology Physiology Pharmacology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> strategies term static predictions preictal state classification investigated quantitative electroencephalography identifying convolutional lstm higher temporal resolution false detection rate benchmarked current state based deep learning acute postnatal period 132 neonates containing 1 &# 8211 seizure occurrence periods seizure alarm system identified optimal performance expected calibration error seizure prediction horizons horizon seizure prediction series dl methods assessed convlstm performance seizure prediction strongest performance model calibration typically focused remains unexplored findings establish eeg data 68 events 281 hours
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Comparison of DL methods using QEEG.

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