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 121–130 of 175 papers

TitleStatusHype
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video ProcessingCode0
A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals—0
An Explainable Model for EEG Seizure Detection based on Connectivity Features—0
RAMSES: A full-stack application for detecting seizures and reducing data during continuous EEG monitoring—0
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures—0
Epileptic Seizures Detection Using Deep Learning Techniques: A Review—0
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification—0
Energy Constraints Improve Liquid State Machine Performance—0
Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling—0
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based 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