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 131–140 of 175 papers

TitleStatusHype
Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection—0
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine—0
Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy—0
MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots—0
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals—0
ADEPOS: A Novel Approximate Computing Framework for Anomaly Detection Systems and its Implementation in 65nm CMOS—0
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals—0
A hybrid automated detection of epileptic seizures in EEG based on wavelet and machine learning techniques—0
A Meta-GNN approach to personalized seizure detection and classification—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