ArticleScientific reports2021
A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management.Current neuropharmacology · 2022Pooled it
- Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction.Scientific reports · 2024Article
- Comparison between epileptic seizure prediction and forecasting based on machine learning.Scientific reports · 2024Article
- An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection.Scientific reports · 2024Article
- EEG epilepsy seizure prediction: the post-processing stage as a chronology.Scientific reports · 2024Article
- On the performance of seizure prediction machine learning methods across different databases: the sample and alarm-based perspectives.Frontiers in neuroscience · 2024Article
- Domain adaptation for EEG-based, cross-subject epileptic seizure prediction.Frontiers in neuroinformatics · 2024Article
- Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics.Network neuroscience (Cambridge, Mass.) · 2024Article
- The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions.Epilepsia open · 2023Review
- Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy.Scientific reports · 2023Article
- Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
- EPIC: Annotated epileptic EEG independent components for artifact reduction.Scientific data · 2022Article
- Weak self-supervised learning for seizure forecasting: a feasibility study.Royal Society open science · 2022Article
- Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm.Scientific reports · 2022Article
- Synthetic Epileptic Brain Activities with TripleGAN.Computational and mathematical methods in medicine · 2022Article
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.Computational and mathematical methods in medicine · 2022Review
- Power efficient refined seizure prediction algorithm based on an enhanced benchmarking.Scientific reports · 2021Article
- Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a critical step. Past approaches failed to attain real-world applicability due to lack of generalization capacity. More recently, deep learning techniques may outperform traditional classifiers and handle time dependencies. However, despite the existing efforts for providing interpretable insights, clinicians may not be willing to make high-stake decisions based on them. Furthermore, a disadvantageous aspect of the more usual seizure prediction pipeline is its modularity and significant independence between stages. An alternative could be the construction of a search algorithm that, while considering pipeline stages' synergy, fine-tunes the selection of a reduced set of features that are widely used in the literature and computationally efficient. With extracranial recordings from 19 patients suffering from temporal-lobe seizures, we developed a patient-specific evolutionary optimization strategy, aiming to generate the optimal set of features for seizure prediction with a logistic regression classifier, which was tested prospectively in a total of 49 seizures and 710 h of continuous recording and performed above chance for 32% of patients, using a surrogate predictor. These results demonstrate the hypothesis of pre-ictal period identification without the loss of interpretability, which may help understanding brain dynamics leading to seizures and improve prediction algorithms.
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