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 101–110 of 175 papers

TitleStatusHype
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
A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures—0
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification—0
Scaling convolutional neural networks achieves expert-level seizure detection in neonatal EEG—0
Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm—0
Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm—0
Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics—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