SOTAVerified

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

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Showing 61–70 of 175 papers

TitleStatusHype
Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics—0
Enhancing Epileptic Seizure Detection with EEG Feature Embeddings—0
Privacy-preserving Early Detection of Epileptic Seizures in VideosCode0
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems—0
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling—0
Ongoing EEG artifact correction using blind source separation—0
Reporting existing datasets for automatic epilepsy diagnosis and seizure detection—0
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals—0
Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis—0
Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals—0
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All datasetsTUH EEG Seizure CorpusCHB-MIT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet+ LSTMAUROC0.92—Unverified
2CNN2D+LSTMAUROC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1TF-Tensor-CNNAccuracy89.63—Unverified