Key result
An automatic seizure detection system using convolutional neural networks achieved 99.02% accuracy and 92.35% sensitivity for seizure detection in a cross-patient scenario.
Why the study?
Does an automatic seizure detection system using convolutional neural networks achieve high accuracy and sensitivity in detecting seizures from EEG signals?
Population
Electroencephalogram signals from the Children's Hospital Boston-Massachusetts Institute of Technology…
Authors
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Supports CNN-based seizure detection in mobile frameworks; leaves open prospective clinical validation before adoption.
Does an automatic seizure detection system using convolutional neural networks achieve high accuracy and sensitivity in detecting seizures from EEG signals?
A proposed mobile multimedia framework using convolutional neural networks for automatic seizure detection achieved high accuracy and sensitivity on a standard EEG database.
Muhammad et al. (2018) studied Seizure. Automatic seizure detection system using convolutional neural networks was evaluated on System accuracy and sensitivity in a cross-patient scenario. An automatic seizure detection system using convolutional neural networks achieved 99.02% accuracy and 92.35% sensitivity for seizure detection in a cross-patient scenario.
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