ArticleScientific reports2024
The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Trial
- Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.Neurology and therapy · 2026Review
- A Multifractal-Guided Machine Learning Framework for Late Post-Traumatic Seizure Prediction Following Hemorrhagic Traumatic Brain Injury.Research square · 2026Article
- Development and validation of a machine learning model to predict prognostic outcomes in infantile epileptic spasms syndrome.Frontiers in pediatrics · 2026Article
- Non-invasive preoperative prediction of surgical outcomes in drug-resistant epilepsy using seizure-related EEG network dynamics.Frontiers in neuroscience · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.
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