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

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 minute 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 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.