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

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Showing 61–70 of 175 papers

TitleStatusHype
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
A hybrid automated detection of epileptic seizures in EEG based on wavelet and machine learning techniques—0
BUNDL: Bayesian Uncertainty-aware Deep Learning with Noisy training Labels for Seizure Detection in EEG—0
BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring—0
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models—0
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine—0
A Novel Matrix Representation of Discrete Biomedical Signals—0
EEG Signal Processing using Wavelets for Accurate Seizure Detection through Cost Sensitive Data Mining—0
EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units—0
BioSerenity-E1: a self-supervised EEG model for medical applications—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