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medical-8

Seizure Detection

3260 papers • 126 benchmarks • 313 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

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Benchmarks

These leaderboards are used to track progress in seizure-detection-8

Trend

Dataset

Best Model

Actions

TUH EEG Seizure Corpus

TUH EEG Seizure Corpus

CHB-MIT

CHB-MIT

Libraries

i

Use these libraries to find seizure-detection-8 models and implementations

Datasets

CHB-MIT

TUH EEG Seizure Corpus

TUH EEG Seizure Corpus

BIDS CHB-MIT Scalp EEG Database

BIDS CHB-MIT Scalp EEG Database

Siena Scalp EEG Database

Siena Scalp EEG Database

SeizeIT1

BIDS Siena Scalp EEG Database

BIDS Siena Scalp EEG Database

Subtasks

No subtasks available.

Most implemented papers

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

L. Benini, Francesco Conti, R. Schilling, Pasquale Davide Schiavone, A. Pullini, D. Rossi, Frank K. Gürkaynak, M. Muehlberghuber, Michael Gautschi, Igor Loi, Germain Haugou, S. Mangard•Sat Dec 17 2016

Fulmine, a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks is proposed.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

A dataset of neonatal EEG recordings with seizures annotations

A dataset of neonatal EEG recordings with seizures annotations

SPaRCNet

120

0

SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

S. Harrer, Jianbin Tang, Umar Asif, Subhrajit Roy•Thu Mar 07 2019

SeizureNet, a deep learning framework which learns multi-spectral feature embeddings using an ensemble architecture for cross-patient seizure type classification, is presented and shown to improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints.

88 0

An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

Ikaro Silva, George B. Moody•Tue Sep 23 2014

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet’s software and databases and should easily be able to reproduce, validate, and compare results published based on Physio net's software and database.

207 0

VSViG: Real-Time Video-Based Seizure Detection via Skeleton-Based Spatiotemporal ViG

Yankun Xu, Junzhe Wang, Yun-Hsuan Chen, Jie Yang, Wenjie Ming, Shuangquan Wang, Mohamad Sawan•Thu Nov 23 2023

This work proposes a novel Video-based Seizure detection model via a skeleton-based spatiotemporal Vision Graph neural network (VSViG), which outperforms previous state-of-the-art action recognition models on collected patients' video data with higher accuracy, lower FLOPs, and smaller model size.

5 0

Learning Robust Features using Deep Learning for Automatic Seizure Detection

Joelle Pineau, Pierre Thodoroff, Andrew Lim•Sat Jul 30 2016

The proposed approach exceeds significantly previous results obtained on cross-patient classifiers both in terms of sensitivity and false positive rate and proves to be robust to missing channel and variable electrode montage.

312 0

Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds

C. Alippi, Daniele Grattarola, Daniele Zambon, L. Livi•Tue May 15 2018

A novel change detection framework based on neural networks and CCMs, which takes into account the non-Euclidean nature of graphs is introduced, and the proposed methods are able to detect even small changes in a graph-generating process, consistently outperforming approaches based on Euclidean embeddings.

44 0

Seizure Type Classification Using EEG Signals and Machine Learning: Setting a Benchmark

S. Harrer, S. Roy, U. Asif, J. Tang•Fri Dec 04 2020

Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of drugs but also the range of activities a patient can safely engage in. With recent advances being made towards artificial intelligence enabled automatic seizure detection, the next frontier is the automatic classification of seizure types. On that note, in this paper, we explore the application of machine learning algorithms for multiclass seizure type classification. We used the recently released TUH EEG seizure corpus (v1.4.0 and v1.5.2) and conducted a thorough search space exploration to evaluate the performance of a combination of various preprocessing techniques, machine learning algorithms, and corresponding hyperparameters on this task. We show that our algorithms can reach a weighted F1 score of up to 0.901 for seizure-wise cross validation and 0.561 for patient-wise cross validation thereby setting a benchmark for scalp EEG based multi-class seizure type classification.

59 0

Synthetic Epileptic Brain Activities Using Generative Adversarial Networks

P. Ryvlin, Damian Pascual, Roger Wattenhofer, David Atienza Alonso, Amir Aminifar•Sun Jul 21 2019

This work generates 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.

12 0

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Lina Yao, Xiang Zhang, Manqing Dong, Zhe Liu, Yu Zhang, Yong Li•Tue Sep 17 2019

A robust and explainable epileptic seizure detection model that effectively learns from seizure states while eliminates the inter-patient noises is proposed and an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure is developed.

96 0

Adding a benchmark result helps the community track progress.

Seizure Detection | State-of-the-Art