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 21–30 of 175 papers

TitleStatusHype
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmarkCode0
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health SystemsCode0
ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform DischargesCode0
Learning Robust Features using Deep Learning for Automatic Seizure DetectionCode0
MICAL: Mutual Information-Based CNN-Aided Learned FactorCode0
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure DetectionCode0
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure DetectionCode0
Learning Robust Representations of Tonic-Clonic Seizures With Cyclic TransformerCode0
Ensemble learning using individual neonatal data for seizure detectionCode0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
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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