Seizure prediction
8 papers with code • 1 benchmarks • 1 datasets
Latest papers
A library of quantitative markers of seizure severity
In individual patients, 71% had a moderate to large difference (ranksum r > 0. 3) between focal and subclinical seizures in three or more markers.
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly.
Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm
We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients.
Searching for waveforms on spatially-filtered epileptic ECoG
Seizures are one of the defining symptoms in patients with epilepsy, and due to their unannounced occurrence, they can pose a severe risk for the individual that suffers it.
A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients.
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.
Predicting epileptic seizures using nonnegative matrix factorization
The resulting method yields a computationally and conceptually simple, interpretable model of EEG signals of preictal and interictal states, which shows a good performance for the task of seizure prediction.
Learning Optimized Risk Scores
Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers.