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
Analog Seizure Detection for Implanted Responsive Neurostimulation—0
Analysis of Cardiovascular Changes Caused by Epileptic Seizures in Human Photoplethysmogram Signal—0
An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers—0
An Explainable Model for EEG Seizure Detection based on Connectivity Features—0
A Novel Matrix Representation of Discrete Biomedical Signals—0
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models—0
An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
A review on Epileptic Seizure Detection using Machine Learning—0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—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