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 31–40 of 175 papers

TitleStatusHype
Learning Robust Features using Deep Learning for Automatic Seizure DetectionCode0
LightFF: Lightweight Inference for Forward-Forward AlgorithmCode0
Efficient Epileptic Seizure Detection Using CNN-Aided Factor GraphsCode0
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and OctaveCode0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure DetectionCode0
Learning Robust Representations of Tonic-Clonic Seizures With Cyclic TransformerCode0
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature ManifoldsCode0
Ensemble learning using individual neonatal data for seizure detectionCode0
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmarkCode0
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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