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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	Score
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment	Dec 18, 2024	Brain Computer InterfaceDiagnostic	—Unverified	0	0
Deep Architectures for Automated Seizure Detection in Scalp EEGs	Dec 28, 2017	EEGElectroencephalogram (EEG)	—Unverified	0	0
Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection	Aug 28, 2017	EEGElectroencephalogram (EEG)	—Unverified	0	0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information	Jan 12, 2021	EEGElectroencephalogram (EEG)	—Unverified	0	0
Deep Classification of Epileptic Signals	Jan 11, 2018	ClassificationEEG	—Unverified	0	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