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 101–125 of 175 papers

TitleStatusHype
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier—0
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring—0
An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers—0
EEG Signal Processing using Wavelets for Accurate Seizure Detection through Cost Sensitive Data Mining—0
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies—0
Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm—0
Shift-invariant waveform learning on epileptic ECoG—0
Efficient Epileptic Seizure Detection Using CNN-Aided Factor GraphsCode0
Automatic Seizure Detection Using the Pulse Transit Time—0
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices—0
Analog Seizure Detection for Implanted Responsive Neurostimulation—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture—0
Deep Learning for EEG Seizure Detection in Preterm Infants—0
Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure DetectionCode0
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
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video ProcessingCode0
A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals—0
An Explainable Model for EEG Seizure Detection based on Connectivity Features—0
RAMSES: A full-stack application for detecting seizures and reducing data during continuous EEG monitoring—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