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 41–50 of 175 papers

TitleStatusHype
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: systematic review—0
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection—0
Convolutional neural network for detection and classification of seizures in clinical data—0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—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
Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection—0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—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