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 51–60 of 175 papers

TitleStatusHype
Deep density ratio estimation for change point detection—0
An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection—0
Deep Learning Approaches for Seizure Video Analysis: A Review—0
Deep Learning for EEG Seizure Detection in Preterm Infants—0
Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction—0
Deep Recurrent Neural Networks for seizure detection and early seizure detection systems—0
Designing Pre-training Datasets from Unlabeled Data for EEG Classification with Transformers—0
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
A hybrid automated detection of epileptic seizures in EEG based on wavelet and machine learning techniques—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