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 161–170 of 175 papers

TitleStatusHype
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature ManifoldsCode0
Learning Robust Features using Deep Learning for Automatic Seizure DetectionCode0
Efficient Epileptic Seizure Detection Using CNN-Aided Factor GraphsCode0
ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform DischargesCode0
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure DetectionCode0
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model MonitoringCode0
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and OctaveCode0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Towards Interpretable Seizure Detection Using WearablesCode0
The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy 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