Seizure Detection

18 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

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.

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.

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.

Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing

fpgdubost/CIFAR-10-Sparsely-Labeled-Sequential-Data 28 Nov 2020

Then, we leverage the proposed training strategy to tackle a real-life application: processing continuous video recordings of epilepsy patients to improve seizure detection, and show that our method outperforms baseline labeling methods by 10 points of average precision.

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.