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 41–50 of 175 papers

TitleStatusHype
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and OctaveCode0
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health SystemsCode0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges—0
Automated Human Mind Reading Using EEG Signals for Seizure Detection—0
Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior—0
An Explainable Model for EEG Seizure Detection based on Connectivity Features—0
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates—0
Audio-Based Epileptic Seizure Detection—0
Show:102550
← PrevPage 5 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