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 151–160 of 175 papers

TitleStatusHype
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models—0
A hybrid automated detection of epileptic seizures in EEG based on wavelet and machine learning techniques—0
Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller—0
Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance—0
Change Point Methods on a Sequence of Graphs—0
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature ManifoldsCode0
Epileptic Seizure Detection: A Deep Learning Approach—0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—0
Deep Classification of Epileptic Signals—0
Optimizing Channel Selection for Seizure Detection—0
Show:102550
← PrevPage 16 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