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

Most implemented papers

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

pulp-platform/pulp 18 Dec 2016

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

IBM/seizure-type-classification-tuh 8 Mar 2019

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

esl-epfl/sz-validation-framework 20 Feb 2024

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

MIT-LCP/wfdb-python Journal of Open Research Software 2014

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 24 Nov 2023

An accurate and efficient epileptic seizure onset detection can significantly benefit patients.

Learning Robust Features using Deep Learning for Automatic Seizure Detection

Sharad24/Epileptic-Seizure-Detection 31 Jul 2016

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

danielegrattarola/cdt-ccm-aae 16 May 2018

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

IBM/seizure-type-classification-tuh 4 Feb 2019

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

dapascual/GAN_epilepsy 22 Jul 2019

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 18 Sep 2019

Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.