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 131–140 of 175 papers

TitleStatusHype
Epileptic Seizure Detection and Classification using Time-Frequency Features in EEG Signals—0
Analysis of Cardiovascular Changes Caused by Epileptic Seizures in Human Photoplethysmogram Signal—0
Neural Memory Networks for Seizure Type Classification—0
ADEPOS: A Novel Approximate Computing Framework for Anomaly Detection Systems and its Implementation in 65nm CMOS—0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Audio-Based Epileptic Seizure Detection—0
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
Synthetic Epileptic Brain Activities Using Generative Adversarial NetworksCode0
Ranking power spectra: a proof of concept—0
Deep density ratio estimation for change point detection—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