Details

Autor(en) / Beteiligte

• Mercier, Mattia
• Pepi, Chiara
• Carfi-Pavia, Giusy
• De Benedictis, Alessandro
• Espagnet, Maria Camilla Rossi
• Pirani, Greta
• Vigevano, Federico
• Marras, Carlo Efisio
• Specchio, Nicola
• De Palma, Luca

Titel

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

Ist Teil von

• Scientific reports, 2024-05, Vol.14 (1), p.10887-10887, Article 10887

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2024

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.