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 11–20 of 175 papers

TitleStatusHype
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViGCode1
BIOT: Cross-data Biosignal Learning in the WildCode1
ManyDG: Many-domain Generalization for Healthcare ApplicationsCode1
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space ModelsCode1
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural NetworksCode1
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
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisCode1
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsCode1
EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network—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