ArticleScientific reports2020
Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Graph theory in paediatric epilepsy: A systematic review.Dialogues in clinical neuroscience · 2021Pooled it
- Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.Neurology and therapy · 2026Review
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- NIPS: Network Inference with Partial State measurements using forced-delay embedding.PNAS nexus · 2026Article
- Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks.Brain sciences · 2025Article
- Developing a Vital Signal Detection Electrode for Fabric Substrate Using a High-Performance Conductive Carbon-Based Ink.IEEE open journal of engineering in medicine and biology · 2025Article
- A Real-Time Epilepsy Detection Method Using Embedded Zero Tree Wavelet Approach and Support Vector Machine.Behavioural neurology · 2025Article
- Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.Sensors (Basel, Switzerland) · 2024Review
- Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Article
- Article
- Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images.Sensors (Basel, Switzerland) · 2023Article
- On quantification and maximization of information transfer in network dynamical systems.Scientific reports · 2023Article
- Personalized tDCS for Focal Epilepsy-A Narrative Review: A Data-Driven Workflow Based on Imaging and EEG Data.Brain sciences · 2022Review
- Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG.Sensors (Basel, Switzerland) · 2022Article
- A survey of brain network analysis by electroencephalographic signals.Cognitive neurodynamics · 2022Article
- Automatic seizure detection with different time delays using SDFT and time-domain feature extraction.Journal of biomedical research · 2022Article
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- The role of network connectivity on epileptiform activity.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (EEG). As a noninvasive monitoring method to record brain electrical activities, EEG has been widely used for capturing the underlying dynamics of disruptive neuronal responses across the brain in real-time to provide clinical guidance in support of epileptic seizure treatments in practice. In this study, we introduce a novel dynamic learning method that first infers a time-varying network constituted by multivariate EEG signals, which represents the overall dynamics of the brain network, and subsequently quantifies its topological property using graph theory. We demonstrate the efficacy of our learning method to detect relatively strong synchronization (characterized by the algebraic connectivity metric) caused by abnormal neuronal firing during a seizure onset. The computational results for a realistic scalp EEG database show a detection rate of 93.6% and a false positive rate of 0.16 per hour (FP/h); furthermore, our method observes potential pre-seizure phenomena in some cases.
Indexed as
Identifiers
What 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.