NLP-based tools for localization of the Epileptogenic Zone in patients with drug-resistant focal epilepsy

Abstract

Background: Drug-resistant focal epilepsy, defined by failure of two antiepileptic drugs, affects about 30% of patients with epilepsy. Epilepsy surgery may represent an alternative options for this population. However, defining the epileptogenic zone to be surgically removed requires highly specialised medical expertise as well as advanced technologies. The aim of this work is building a cost-effective support system based on text, in particular based on the semiological descriptions of the seizures (temporal vs extratemporal lobe; right vs left hemisphere), in order to predict the localization of seizure origin. Methods: Among a population of 121 surgically treated and seizure-free drug-resistant patients suffering with focal epilepsy, recruited at the Niguarda Hospital in Milan, we extracted a total number of 509 descriptions of seizures. After a data pre-processing phase, we used natural language processing tools to build numerical representations of the seizures descriptions, both using embedding and count-based methods. We then used machine learning models performing a binary classification into right/left and temporal/extra-temporal. Results: All predictive models show a better performance when using the representations relying on embedding models respect to count-based ones. Between all the combinations of representations and classifiers, the best performance obtained in terms of F1-score is 84.7% ± 0.6. Discussion: This preliminary work reached encouraging results considering both localization tasks. The main advantage is that no specific knowledge about epilepsy is used to build the models, rendering our pipeline applicable also in other scenarios. The major limitation lies in the fact that the text is highly specific to the writer.