The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.
Sci Rep
; 14(1): 10887, 2024 05 13.
Article
in En
| MEDLINE
| ID: mdl-38740844
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Electroencephalography
/
Machine Learning
Limits:
Adolescent
/
Child
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Child, preschool
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Female
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Humans
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Infant
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Male
Language:
En
Journal:
Sci Rep
Year:
2024
Document type:
Article
Affiliation country:
Italia