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medical-7

Seizure prediction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Dataset

Best Model

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Melbourne University Seizure Prediction

Libraries

Use these libraries to find seizure-prediction-7 models and implementations

Datasets

TUH EEG Seizure Corpus

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Most implemented papers

Learning Optimized Risk Scores

C. Rudin, Berk Ustun•Fri Sep 30 2016

A new machine learning approach to learn risk scores that can fit risk scores in a way that scales linearly in the number of samples, provides a certificate of optimality, and obeys real-world constraints without parameter tuning or post-processing is presented.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals

Y. Li, Shuyue Jia, Yimin Hou, Yan Shi•Wed Jun 24 2020

An attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents from raw EEG signals, where the topological structure of EEG electrodes was built as a graph, and results were promising that the implementation of the graph-structured topology was superior to decode raw EEG data.

25 0

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A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction

Mauro F. Pinto, A. Leal, Fábio Lopes, Pedro Martins, C. Teixeira, A. Dourado•Mon Feb 08 2021

A patient-specific evolutionary optimization strategy is developed, aiming to generate the optimal set of features for seizure prediction with a logistic regression classifier, which performed above chance for 32% of patients, using a surrogate predictor.

51 0

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Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Mauro F. Pinto, Tiago Coelho, A. Leal, Fábio Lopes, A. Dourado, Pedro Martins, C. Teixeira•Mon Mar 14 2022

An evolutionary seizure prediction model is developed that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort, and provides patient-specific interpretable insights that might contribute to a better understanding of seizure generation processes and explain the algorithm’s decisions.

29 0

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Searching for waveforms on spatially-filtered epileptic ECoG

Carlos H. Mendoza-Cardenas, A. Brockmeier•Wed Mar 24 2021

This work shows that data-driven waveform learning methods have the potential to not only contribute features with predictive power for seizure prediction, but also to facilitate the discovery of oscillatory patterns that could contribute to the understanding of the pathophysiology and etiology of seizures.

1 0

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Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

M. Azghadi, C. Lammie, Chenqi Li, Xuening Dong, A. Amirsoleimani, R. Genov•Sun Jun 19 2022

A novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy, and a stuck weight offsetting methodology to mitigate performance degradation due to stuck memristor weights.

28 0

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A library of quantitative markers of seizure severity

B. Diehl, Sarah J Gascoigne, Leonard Waldmann, Gabrielle M. Schroeder, Mariella Panagiotopoulou, J. Blickwedel, F. Chowdhury, Alison Cronie, J. Duncan, J. Falconer, Ryan Faulder, Y. Guan, V. Leach, Shona Livingstone, Christoforos A. Papasavvas, R. Thomas, Kevin L. Wilson, Peter N Taylor, Yujiang Wang•Wed Jun 29 2022

A library of quantitative EEG markers that assess the spread and intensity of abnormal electrical activity during and after seizures is developed.

15 0

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Seizure prediction | State-of-the-Art