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

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Showing 101–150 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
Epileptic Seizures Detection Using Deep Learning Techniques: A Review—0
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures—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
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
Audio-Based Epileptic Seizure Detection—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
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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