Evaluation of ictal electro-clinical findings using standardised feature extraction and machine learning
Background: Semiology and EEG provide important electro-clinical information about epileptic seizures. However, in clinical practice these aspects are reported in free-text, which hinders further analysis of large datasets.
Objective: to evaluate the clinical significance of the electro-clinical findings extracted using a standardised system, and then analyzed using a machine learning approach.
Methods: Electoclinical features, comprising seizure semiology and ictal EEG will be retrospectively extracted form a large anonymised database. The features will be extracted and stored using the SCORE system. Gold standard will be extracted from long-term (>1 year) follow-up. Correlation with the focus localization, prognosis and therapeutic outcome will me analyzed using a machine learning approach.
Expected results: This systematic approach will alow us to provide clinicians with evidence-based information on the diagnostic value of the ictal electro-clinical features. This will improve diagnosis, classification and presurgical evaluation of patients with epilepsy.
Phycisian, specialist in neurology, with documented clinical and research experience with seizure semiology, EEG and machine learning
How to apply
Please submit your application via this link. Application deadline is 13 May 2021 23:59 CET. Preferred starting date is 1 September 2021.
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Please contact Professor Sándor Beniczky, email@example.com for further information.
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