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 26–50 of 175 papers

TitleStatusHype
Towards Interpretable Seizure Detection Using WearablesCode0
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type ClassificationCode0
Privacy-preserving Early Detection of Epileptic Seizures in VideosCode0
MICAL: Mutual Information-Based CNN-Aided Learned FactorCode0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure DetectionCode0
LightFF: Lightweight Inference for Forward-Forward AlgorithmCode0
Learning Robust Features using Deep Learning for Automatic Seizure DetectionCode0
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure DetectionCode0
Learning Robust Representations of Tonic-Clonic Seizures With Cyclic TransformerCode0
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmarkCode0
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video ProcessingCode0
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature ManifoldsCode0
Efficient Epileptic Seizure Detection Using CNN-Aided Factor GraphsCode0
Ensemble learning using individual neonatal data for seizure detectionCode0
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and OctaveCode0
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health SystemsCode0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges—0
Automated Human Mind Reading Using EEG Signals for Seizure Detection—0
Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior—0
An Explainable Model for EEG Seizure Detection based on Connectivity Features—0
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates—0
Audio-Based Epileptic Seizure 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