Seizure Detection
26 papers with code • 2 benchmarks • 3 datasets
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
Benchmarks
These leaderboards are used to track progress in Seizure Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Datasets
CHB-MIT
A dataset of neonatal EEG recordings with seizures annotations
Most implemented papers
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline.
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease.
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave
The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases.
Learning Robust Features using Deep Learning for Automatic Seizure Detection
We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures.
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs.
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark
On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification.
Synthetic Epileptic Brain Activities Using Generative Adversarial Networks
In this work, we generate synthetic seizure-like brain electrical activities, i. e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for recorded data.
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
xiangzhang1015/adversarial_seizure_detection
•
•
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times.
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
tsy935/eeg-gnn-ssl
•
•
ICLR 2022
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment.
