Seizure Detection
38 papers with code • 2 benchmarks • 8 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
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Datasets
CHB-MIT
TUH EEG Seizure Corpus
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.
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms
Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics.
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.
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
xuyankun/stvig-for-reads-v
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An accurate and efficient epileptic seizure onset detection can significantly benefit patients.
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
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Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
