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 76–100 of 175 papers

TitleStatusHype
A review on Epileptic Seizure Detection using Machine Learning—0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
Six-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in EEG—0
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model MonitoringCode0
Many-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems—0
Improving self-supervised pretraining models for epileptic seizure detection from EEG data—0
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural NetworksCode1
MICAL: Mutual Information-Based CNN-Aided Learned FactorCode0
Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection—0
Energy-Efficient Tree-Based EEG Artifact Detection—0
Ensemble learning using individual neonatal data for seizure detectionCode0
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection—0
Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy—0
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning—0
Significant Low-dimensional Spectral-temporal Features for Seizure Detection—0
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure DetectionCode0
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic SettingCode1
Scalable Machine Learning Architecture for Neonatal Seizure Detection on Ultra-Edge DevicesCode1
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure DetectionCode0
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection—0
Automated Human Mind Reading Using EEG Signals for Seizure Detection—0
Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm—0
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier—0
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring—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