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Seizure Detection

Seizure Detection is a binary supervised classification problem with the aim of classifying between seizure and non-seizure states of a patient.

Source: ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 171–175 of 175 papers

Title	Date	Tasks	Status	Hype
Privacy-preserving Early Detection of Epileptic Seizures in Videos	Sep 15, 2023	Knowledge DistillationOptical Flow Estimation	CodeCode Available	0
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems	May 9, 2024	Federated LearningPrivacy Preserving	CodeCode Available	0
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification	Mar 8, 2019	ClassificationEEG	CodeCode Available	0
Ensemble learning using individual neonatal data for seizure detection	Apr 11, 2022	EEGElectroencephalogram (EEG)	CodeCode Available	0
Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection	Jun 4, 2024	EEGElectroencephalogram (EEG)	CodeCode Available	0

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All datasets TUH EEG Seizure Corpus CHB-MIT

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet+ LSTM	AUROC	0.92	—	Unverified
2	CNN2D+LSTM	AUROC	0.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TF-Tensor-CNN	Accuracy	89.63	—	Unverified