CONICET | Buscador de Institutos y Recursos Humanos

Refractory epilepsy poses a significant clinical challenge, as some patients do not respond to pharmacological treatments targeting the characteristic seizures of the disease. While surgical resection of the affected regions can be an effective intervention, it is not always feasible, forcing patients to cope with a diminished quality of life. The use of intracranial electrodes (iEEG) provides signals with high spatial and temporal resolution, allowing the identification of three principal brain states: basal, postictal, and preictal. Early detection of the preictal state is critical, as it facilitates seizure prediction several minutes in advance.In this study, mutual information (MI) was utilized to analyze both linear and nonlinear statistical dependencies in multichannel (iEEG) time series. By quantifying information transmission across neural rhythms associated with state epilepticus, MI demonstrated robustness as a biomarker with significant potential for early seizure detection. Furthermore, MI-based metrics derived from different frequency bands were incorporated into supervised learning models, enabling accurate classification of the preictal state with 81% accuracy.This preliminary study, conducted on data from four patients, suggests that MI-based analysis may represent a promising biomarker for the detection of the preictal state. Nevertheless, the generalization of these findings requires further validation in future studies involving larger and more heterogeneous cohorts. By facilitating seizure prediction at an early stage, this approach holds promise as a tool to improve the quality of life for patients with refractory epilepsy. ( See Link https://authors.elsevier.com/a/1mRCI6DBR3Kxkd )

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