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 11–20 of 175 papers

TitleStatusHype
A foundation model with multi-variate parallel attention to generate neuronal activityCode1
SeizureTransformer: Scaling U-Net with Transformer for Simultaneous Time-Step Level Seizure Detection from Long EEG RecordingsCode1
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithmsCode1
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsCode1
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisCode1
BIOT: Cross-data Biosignal Learning in the WildCode1
Deep Latent Variable Modeling of Physiological SignalsCode1
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViGCode1
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural NetworksCode1
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video ProcessingCode0
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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