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 111–120 of 175 papers

TitleStatusHype
Deep Learning for EEG Seizure Detection in Preterm Infants—0
Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture—0
Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure DetectionCode0
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisCode1
Nocturnal Seizure Detection Using Off-the-Shelf WiFi—0
Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection—0
Few-shot time series segmentation using prototype-defined infinite hidden Markov models—0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information—0
Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals—0
Edge Deep Learning for Neural Implants—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