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 141–150 of 175 papers

TitleStatusHype
Deep density ratio estimation for change point detection—0
Temporal Graph Convolutional Networks for Automatic Seizure Detection—0
Convolutional neural network for detection and classification of seizures in clinical data—0
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type ClassificationCode0
Epileptic seizure classification using statistical sampling and a novel feature selection algorithm—0
Dynamical Component Analysis (DyCA) and its application on epileptic EEG—0
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmarkCode0
Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network—0
A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures—0
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures—0
Show:102550
← PrevPage 15 of 18Next →
All datasetsTUH EEG Seizure CorpusCHB-MIT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet+ LSTMAUROC0.92—Unverified
2CNN2D+LSTMAUROC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1TF-Tensor-CNNAccuracy89.63—Unverified