AI Digital Twin: Chiari with LMS Adaptive Filtering

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

https://doi.org/10.5281/zenodo.19926992

Version 1.0.2 (252 KB)
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22 May 2026

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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.

Cite As

Icaro dos Santos (2026). AI Digital Twin: Chiari with LMS Adaptive Filtering (https://au.mathworks.com/matlabcentral/fileexchange/183802-ai-digital-twin-chiari-with-lms-adaptive-filtering), MATLAB Central File Exchange. Retrieved .

Acknowledgements

Inspired by: FFmpeg Toolbox

General Information

MATLAB Release Compatibility

  • Compatible with any release

Platform Compatibility

  • Windows
  • macOS
  • Linux
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Version Published Release Notes
1.0.2

Updated title and summary for better discoverability. Added clearer description of LMS adaptive filtering and Simulink implementation. Linked to full Zenodo lab document.
1.0.1

Updated repository metadata with a representative cover image. No changes were made to the underlying Simulink models or MATLAB scripts.
1.0.0