Dynamotypes for Dummies: A Toolbox, Atlas, and Tutorial for Simulating a Comprehensive Range of Realistic Synthetic...

Area of Science:

• Computational neuroscience
• Dynamical systems theory
• Epileptology

Background:

• Epileptic seizures represent a transition from normal brain activity to abnormal synchronized bursting.
• Dynamical systems theory provides a framework for understanding these transitions as bifurcations.
• A prior model simulated 16 seizure "dynamotypes" based on first-order dynamics.

Purpose of the Study:

• To develop a tool for implementing and understanding a comprehensive seizure model.
• To generate a wide range of synthetic seizures with realistic characteristics.
• To provide an educational resource on seizure bifurcations and their dynamical principles.

Main Methods:

• Development of a dynamical atlas detailing all 16 onset-offset bifurcation combinations.
• Implementation of a graphical user interface for generating diverse simulated seizures.
• Inclusion of methods for adding realistic noise and filtering to simulated electroencephalogram (EEG) data.

Main Results:

• A dynamical atlas characterizing distinct features for each of the 16 seizure onset-offset bifurcation types was created.
• A user-friendly toolbox was developed for generating synthetic seizures.
• Simulated seizures exhibit realistic noise and filtering, mimicking human EEG data.

Conclusions:

• The developed toolbox serves as an educational tool demonstrating seizure bifurcation principles.
• It provides algorithms for generating diverse, realistic synthetic seizure patterns.
• This generative model can enhance seizure detection algorithm training and clinical understanding of brain dynamics.