ReviewCurrent neurology and neuroscience reports2015
Seizure Prediction: Science Fiction or Soon to Become Reality?
Review in Current neurology and neuroscience reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.Current aging science · 2025Article
- Article
- Neurosurgery and the Brain-Computer Interface.Advances in experimental medicine and biology · 2024Review
- Seizure forecasting: Bifurcations in the long and winding road.Epilepsia · 2023Review
- Network-motif delay differential analysis of brain activity during seizures.Chaos (Woodbury, N.Y.) · 2023Article
- Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy.Scientific reports · 2023Article
- Weak self-supervised learning for seizure forecasting: a feasibility study.Royal Society open science · 2022Article
- Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2022Article
- Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast.Epilepsia · 2021Article
- Active probing to highlight approaching transitions to ictal states in coupled neural mass models.PLoS computational biology · 2021Article
- Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond.Frontiers in neural circuits · 2021Review
- Critical slowing down as a biomarker for seizure susceptibility.Nature communications · 2020Article
- Prospective validation study of an epilepsy seizure risk system for outpatient evaluation.Epilepsia · 2020Article
- Viability of Preictal High-Frequency Oscillation Rates as a Biomarker for Seizure Prediction.Frontiers in human neuroscience · 2020Article
- Real-time epileptic seizure prediction based on online monitoring of pre-ictal features.Medical & biological engineering & computing · 2019Article
- Seizure prediction - ready for a new era.Nature reviews. Neurology · 2018Review
- Seizure pathways: A model-based investigation.PLoS computational biology · 2018Article
- Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG.Brain : a journal of neurology · 2018Article
- Fractal and Multifractal Properties of Electrographic Recordings of Human Brain Activity: Toward Its Use as a Signal Feature for Machine Learning in Clinical Applications.Frontiers in physiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.
Indexed as
Identifiers
26404726What OpenQuestion holds
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.