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 71–80 of 175 papers

TitleStatusHype
Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction—0
EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units—0
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space ModelsCode1
Topological biomarkers for real-time detection of epileptic seizures—0
A Meta-GNN approach to personalized seizure detection and classification—0
A review on Epileptic Seizure Detection using Machine Learning—0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
Six-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in EEG—0
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model MonitoringCode0
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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