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
Epileptic Seizures Detection Using Deep Learning Techniques: A Review—0
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification—0
Energy Constraints Improve Liquid State Machine Performance—0
Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling—0
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification—0
Epileptic Seizure Detection and Classification using Time-Frequency Features in EEG Signals—0
Analysis of Cardiovascular Changes Caused by Epileptic Seizures in Human Photoplethysmogram Signal—0
Neural Memory Networks for Seizure Type Classification—0
ADEPOS: A Novel Approximate Computing Framework for Anomaly Detection Systems and its Implementation in 65nm CMOS—0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Audio-Based Epileptic Seizure Detection—0
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
Synthetic Epileptic Brain Activities Using Generative Adversarial NetworksCode0
Ranking power spectra: a proof of concept—0
Deep density ratio estimation for change point detection—0
Temporal Graph Convolutional Networks for Automatic Seizure Detection—0
Convolutional neural network for detection and classification of seizures in clinical data—0
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type ClassificationCode0
Epileptic seizure classification using statistical sampling and a novel feature selection algorithm—0
Dynamical Component Analysis (DyCA) and its application on epileptic EEG—0
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmarkCode0
Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network—0
A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures—0
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures—0
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing—0
Show:102550
← PrevPage 6 of 7Next →
All datasetsTUH EEG Seizure CorpusCHB-MIT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet+ LSTMAUROC0.92—Unverified
2CNN2D+LSTMAUROC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1TF-Tensor-CNNAccuracy89.63—Unverified