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 126–150 of 175 papers

TitleStatusHype
Topological biomarkers for real-time detection of epileptic seizures—0
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices—0
Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection—0
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine—0
Many-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems—0
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals—0
MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection—0
Six-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in EEG—0
Multi-Dimensional Framework for EEG Signal Processing and Denoising Through Tensor-based Architecture—0
Multi-Modal Machine Learning Framework for Automated Seizure Detection in Laboratory Rats—0
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital—0
Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture—0
Neonatal Seizure Detection using Convolutional Neural Networks—0
Neural Memory Networks for Seizure Type Classification—0
Nocturnal Seizure Detection Using Off-the-Shelf WiFi—0
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures—0
Objective evaluation metrics for automatic classification of EEG events—0
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing—0
Ongoing EEG artifact correction using blind source separation—0
Optimizing Channel Selection for Seizure Detection—0
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling—0
RAMSES: A full-stack application for detecting seizures and reducing data during continuous EEG monitoring—0
Ranking power spectra: a proof of concept—0
Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression—0
Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor—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