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 31–40 of 175 papers

TitleStatusHype
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals—0
A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures—0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—0
Analog Seizure Detection for Implanted Responsive Neurostimulation—0
A review on Epileptic Seizure Detection using Machine Learning—0
Change Point Methods on a Sequence of Graphs—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
A Meta-GNN approach to personalized seizure detection and classification—0
MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots—0
An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection—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