ArticleBMC bioinformatics2015
A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph theory in paediatric epilepsy: A systematic review.Dialogues in clinical neuroscience · 2021Pooled it
- Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review.Computational and structural biotechnology journal · 2024Review
- Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach.Epilepsia · 2024Article
- Stability, change, and reliable individual differences in electroencephalography measures: A lifespan perspective on progress and opportunities.NeuroImage · 2023Review
- Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG.Medical & biological engineering & computing · 2022Article
- Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease.Frontiers in computational neuroscience · 2022Article
- EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss.Frontiers in neuroinformatics · 2020Article
- A Novel Modular Headmount Design for non-invasive Scalp EEG Recordings in Awake Animal Models.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2018Article
- A Dual-Mode Magnetic-Acoustic System for Monitoring Fluid Intake Behavior in Animals.IEEE transactions on bio-medical engineering · 2017Article
- Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task.Cognitive neurodynamics · 2016Article
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Authors and funding
10 authors.
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Abstract
backgroundThe lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.
methodsA new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group.
resultsThe study results show the existence of a statistically significant difference (p < 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis.
conclusionsThe current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed.
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