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 1–10 of 175 papers

TitleStatusHype
DBConformer: Dual-Branch Convolutional Transformer for EEG DecodingCode2
Dynamic GNNs for Precise Seizure Detection and Classification from EEG DataCode2
Residual and bidirectional LSTM for epileptic seizure detectionCode2
Deep Latent Variable Modeling of Physiological SignalsCode1
A foundation model with multi-variate parallel attention to generate neuronal activityCode1
ManyDG: Many-domain Generalization for Healthcare ApplicationsCode1
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisCode1
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsCode1
BIOT: Cross-data Biosignal Learning in the WildCode1
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViGCode1
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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