Topological biomarkers for seizure state transitions

Area of Science:

• Neuroscience
• Dynamical Systems
• Topology

Background:

• Detecting critical state transitions in noisy, high-dimensional neural recordings is a significant challenge in nonlinear dynamics.
• Existing methods may struggle with the complexity and noise inherent in neurophysiological signals.

Purpose of the Study:

• To develop and validate a novel topological framework for analyzing state transitions in neurophysiological data.
• To identify robust topological biomarkers for detecting critical events like seizures.

Main Methods:

• Applied geometric, persistent homology analysis to sliding-window reconstructions of multichannel neurophysiological signals (iEEG/EEG/MEG).
• Evaluated two topological biomarkers: finite-difference persistence derivative (rate of topological change) and total persistence (topological complexity).

Main Results:

• The persistence derivative robustly aligned with seizure onset and termination across iEEG/EEG/MEG datasets.
• Total persistence significantly distinguished between ictal and interictal periods.

Conclusions:

• This work introduces an interpretable topological framework for analyzing state transitions in complex neurophysiological dynamics.
• The proposed topological biomarkers offer a promising approach for real-time detection and analysis of critical events in neural activity.