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 71–80 of 175 papers

TitleStatusHype
A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures—0
Convolutional neural network for detection and classification of seizures in clinical data—0
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection—0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—0
Analog Seizure Detection for Implanted Responsive Neurostimulation—0
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: systematic review—0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
A review on Epileptic Seizure Detection using Machine Learning—0
Characterizing Accuracy Trade-offs of EEG Applications on Embedded HMPs—0
Epileptic seizure classification using statistical sampling and a novel feature selection algorithm—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