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Fully Closed Loop Test Environment for Adaptive Implantable Neural Stimulators Using Computational Models
Scott Stanslaski1, Hafsa Farooqi2, David Escobar Sanabria2
1Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455; Neuromodulation Department, Medtronic PLC, Minneapolis, MN 55432.
Journal of Medical Devices
|June 1, 2022
Summary
A new hardware-in-the-loop testing framework uses computational models to validate adaptive deep brain stimulation (DBS) devices. This method ensures the safety and effectiveness of advanced neuromodulation systems before clinical use.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Medical Devices
Background:
- Implantable brain stimulation devices are advancing, increasing the complexity of adaptive neuromodulation therapy.
- Validating these sophisticated systems is challenging due to patient interactions and environmental noise/artifacts.
Purpose of the Study:
- To present a novel hardware-in-the-loop (HIL) testing framework for adaptive deep brain stimulation (DBS) devices.
- To validate the operational correctness and robustness of adaptive DBS systems before animal or human trials.
Main Methods:
- Employed real-time computational models of pathological neural dynamics (epilepsy, Parkinson's disease).
- Utilized a saline tank setup with electrode arrays connected to an adaptive neuromodulation system.
- Integrated a data acquisition system to simulate neural responses and feedback stimulation effects.
Main Results:
- Successfully tested adaptive DBS algorithms for seizure and beta band power suppression in an implantable system (Medtronic Summit RC+S).
- Demonstrated the framework's ability to test systems against environmental noise and stimulation-induced artifacts.
Conclusions:
- The proposed HIL framework provides a systematic method for testing adaptive DBS systems.
- This approach enhances the reliability and safety of neuromodulation devices for neurological disorders.
