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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 161–170 of 175 papers

TitleStatusHype
Gated Recurrent Networks for Seizure Detection—0
The Temple University Hospital Seizure Detection Corpus—0
Objective evaluation metrics for automatic classification of EEG events—0
Deep Architectures for Automated Seizure Detection in Scalp EEGs—0
Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection—0
Neonatal Seizure Detection using Convolutional Neural Networks—0
Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection—0
Deep Recurrent Neural Networks for seizure detection and early seizure detection systems—0
Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection—0
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsCode1
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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