Digital Twins: Chiari Pathophysiology Simulation

Instructional lab on AI medical digital twins, adaptive LMS filtering, and continuous-time physiological control for Chiari modeling.

Overview

This toolbox provides a complete instructional laboratory for integrating data-driven methods into classical control systems education. Designed for senior-level undergraduate biomedical engineering courses (such as Control Systems for Biomedical Applications), it allows students to explore the Chiari condition through simulated medical digital twins.

Key Pedagogical Features

• Adaptive Noise Cancellation: Implements an LMS filter architecture to isolate physiological signals from chaotic disturbance.
• Physiological Control Loops: Models the brainstem and cerebellum using continuous-time transfer functions and transport delays.
• Real & Synthetic Data: Includes pre-extracted cine-MRI tissue displacement data alongside MATLAB scripts for generating synthetic cardiac-synchronous reference signals.

Getting Started

The Simulink models are designed to be plug-and-play. Simply open the .slx files and click the initialization annotation on the canvas to load the _default baseline data. To run the simulation with custom data, users can execute the included extract_mri_displacement.m and ecg_sim.m scripts.

Academic Citation & Full Lab Handout

This toolbox contains the executable models and data files. For the comprehensive 25-page lab handout, complete theoretical derivations, and clinical context, please download the full publication via Zenodo: DOI: https://doi.org/10.5281/zenodo.19926992

Cite As: Icaro dos Santos. (2026). AI Medical Digital Twins: Adaptive Filtering for Chiari Pathophysiology Simulation and Analytical Modeling. Zenodo.