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
Residual and bidirectional LSTM for epileptic seizure detectionCode2
Dynamic GNNs for Precise Seizure Detection and Classification from EEG DataCode2
A foundation model with multi-variate parallel attention to generate neuronal activityCode1
Time series saliency maps: explaining models across multiple domainsCode1
SeizureTransformer: Scaling U-Net with Transformer for Simultaneous Time-Step Level Seizure Detection from Long EEG RecordingsCode1
The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEGCode1
SeizeIT2: Wearable Dataset Of Patients With Focal EpilepsyCode1
Deep Latent Variable Modeling of Physiological SignalsCode1
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithmsCode1
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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