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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 175 papers

TitleStatusHype
Ensemble learning using individual neonatal data for seizure detectionCode0
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection—0
Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy—0
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning—0
Significant Low-dimensional Spectral-temporal Features for Seizure Detection—0
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure DetectionCode0
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure DetectionCode0
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection—0
Automated Human Mind Reading Using EEG Signals for Seizure Detection—0
Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm—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