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
Gated Recurrent Networks for Seizure Detection—0
Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection—0
Importance of methodological choices in data manipulation for validating epileptic seizure detection models—0
Improving Neonatal Care: An Active Dry-Contact Electrode-based Continuous EEG Monitoring System with Seizure Detection—0
Improving self-supervised pretraining models for epileptic seizure detection from EEG data—0
Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals—0
Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance—0
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection—0
Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals—0
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning—0
Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics—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
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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