ArticleInternational journal of neural systems2017
Seizure Forecasting and the Preictal State in Canine Epilepsy.
Article in International journal of neural systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
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Who cites it
18 citing papers in PubMed.
- Diverse species of animal models in epilepsy research: Progress and perspectives.Zoological research · 2025Review
- Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges.Epilepsia open · 2025Review
- Short-horizon neonatal seizure prediction using EEG-based deep learning.PLOS digital health · 2025Article
- Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm.PeerJ. Computer science · 2025Article
- Machine learning algorithms predict canine structural epilepsy with high accuracy.Frontiers in veterinary science · 2024Article
- An overview of machine learning and deep learning techniques for predicting epileptic seizures.Journal of integrative bioinformatics · 2023Review
- [Construction of an epileptic seizure prediction model using a semi-supervised method of generative adversarial and long short term memory network combined with Stockwell transform].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- An effective fusion model for seizure prediction: GAMRNN.Frontiers in neuroscience · 2023Article
- Dogs as a Natural Animal Model of Epilepsy.Frontiers in veterinary science · 2022Review
- Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research.Frontiers in veterinary science · 2022Review
- [Research progress of epileptic seizure predictions based on electroencephalogram signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- Online Prediction of Lead Seizures from iEEG Data.Brain sciences · 2021Article
- Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies.Frontiers in human neuroscience · 2021Article
- Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients.Brain communications · 2021Article
- Performance metrics for online seizure prediction.Neural networks : the official journal of the International Neural Network Society · 2020Article
- Semi-supervised Training Data Selection Improves Seizure Forecasting in Canines with Epilepsy.Biomedical signal processing and control · 2020Article
- A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals.Journal of medical systems · 2018Article
- Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals.Computational intelligence and neuroscience · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.