The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Autor: Mercier M; Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.; Department of Physiology, Behavioural Neuroscience PhD Program, Sapienza University, Rome, Italy., Pepi C; Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy., Carfi-Pavia G; Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy., De Benedictis A; Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy., Espagnet MCR; Neuroradiology Unit, Imaging Department, Bambino Gesù Children's Hospital, 00165, Rome, Italy., Pirani G; Department of Mechanical and Aerospace Engineering - DIMA, Sapienza University of Rome, Rome, Italy., Vigevano F; Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy., Marras CE; Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy., Specchio N; Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy. nicola.specchio@opbg.net., De Palma L; Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 13; Vol. 14 (1), pp. 10887. Date of Electronic Publication: 2024 May 13.
DOI: 10.1038/s41598-024-60622-5
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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