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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 175 papers

TitleStatusHype
Ongoing EEG artifact correction using blind source separation—0
Optimizing Channel Selection for Seizure Detection—0
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling—0
RAMSES: A full-stack application for detecting seizures and reducing data during continuous EEG monitoring—0
Ranking power spectra: a proof of concept—0
Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression—0
Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor—0
Reporting existing datasets for automatic epilepsy diagnosis and seizure detection—0
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification—0
Scaling convolutional neural networks achieves expert-level seizure detection in neonatal EEG—0
Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm—0
Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm—0
Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics—0
Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network—0
Time series segmentation for recognition of epileptiform patterns recorded via Microelectrode Arrays in vitro—0
Shift-invariant waveform learning on epileptic ECoG—0
Significant Low-dimensional Spectral-temporal Features for Seizure Detection—0
SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using SincNet and Variational Autoencoder—0
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier—0
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures—0
Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier—0
Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection—0
Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction—0
Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection—0
SzCORE as a benchmark: report from the seizure detection challenge at the 2025 AI in Epilepsy and Neurological Disorders Conference—0
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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