Hyperbolic embedding of brain networks as a tool for epileptic seizures forecasting

TL;DR

This paper introduces a novel hyperbolic embedding approach for brain networks that accurately predicts epileptic seizures from intracranial EEG data, offering a promising biomarker for seizure forecasting.

Contribution

The study presents a new method combining hyperbolic geometry and machine learning to discriminate preictal states, improving seizure prediction accuracy.

Findings

Achieved 85% accuracy in distinguishing preictal from interictal states.

Predicted seizures within 24 hours with 87% overall accuracy.

Demonstrated hyperbolic embedding as a reliable biomarker for seizure forecasting.

Abstract

The evidence indicates that intracranial EEG connectivity, as estimated from daily resting state recordings from epileptic patients, may be capable of identifying preictal states. In this study, we employed hyperbolic embedding of brain networks to capture non-trivial patterns that discriminate between connectivity networks from days with (preictal) and without (interictal) seizure. A statistical model was constructed by combining hyperbolic geometry and machine learning tools, which allowed for the estimation of the probability of an upcoming seizure. The results demonstrated that representing brain networks in a hyperbolic space enabled an accurate discrimination (85%) between interictal (no-seizure) and preictal (seizure within the next 24 hours) states. The proposed method also demonstrated excellent prediction performances, with an overall accuracy of 87% and an F1-score of 89% (mean Brier score and Brier skill score of 0.12 and 0.37, respectively). In conclusion, our findings indicate that representations of brain connectivity in a latent geometry space can reveal a daily and reliable signature of the upcoming seizure(s), thus providing a promising biomarker for seizure forecasting.