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 41–50 of 175 papers

TitleStatusHype
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital—0
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithmsCode1
Characterizing Accuracy Trade-offs of EEG Applications on Embedded HMPs—0
Time series segmentation for recognition of epileptiform patterns recorded via Microelectrode Arrays in vitro—0
Multi-Modal Machine Learning Framework for Automated Seizure Detection in Laboratory Rats—0
Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer—0
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model—0
Multi-Dimensional Framework for EEG Signal Processing and Denoising Through Tensor-based Architecture—0
An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection—0
Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression—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