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 151–160 of 175 papers

TitleStatusHype
Reporting existing datasets for automatic epilepsy diagnosis and seizure detection—0
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmarkCode0
LightFF: Lightweight Inference for Forward-Forward AlgorithmCode0
Synthetic Epileptic Brain Activities Using Generative Adversarial NetworksCode0
MICAL: Mutual Information-Based CNN-Aided Learned FactorCode0
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video ProcessingCode0
Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure DetectionCode0
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure DetectionCode0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Learning Robust Representations of Tonic-Clonic Seizures With Cyclic TransformerCode0
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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