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 21–30 of 175 papers

TitleStatusHype
SzCORE as a benchmark: report from the seizure detection challenge at the 2025 AI in Epilepsy and Neurological Disorders Conference—0
Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics—0
The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy DetectionCode0
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates—0
Improving Neonatal Care: An Active Dry-Contact Electrode-based Continuous EEG Monitoring System with Seizure Detection—0
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges—0
BioSerenity-E1: a self-supervised EEG model for medical applications—0
MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior—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