Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Biomedical Engineering

First Advisor's Name

Ranu Jung

First Advisor's Committee Title

Committee chair

Second Advisor's Name

James J. Abbas

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Armando Barreto

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Zachary Danziger

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

David D. Fuller

Fifth Advisor's Committee Title

Committee member

Sixth Advisor's Name

Jacob McPherson

Sixth Advisor's Committee Title

Committee member

Keywords

Respiratory control, ventilation, functional electrical stimulation, respiratory pacing, closed-loop control, bioelectronics, spinal cord injury, hypoventilation

Date of Defense

7-10-2019

Abstract

Respiratory pacing can treat ventilatory insufficiency through electrical stimulation of the respiratory muscles, or the respective innervating nerves, to induce ventilation. It avoids some of the adverse effects associated with mechanical ventilation such as risk of diaphragm atrophy and lung damage. However, current respiratory pacing systems provide stimulation in an open-loop manner. This often requires users to undergo frequent tuning sessions with trained clinicians if the specified stimulation parameters are unable to induce sufficient ventilation in the presence of time-varying changes in muscle properties, chest biomechanics, and metabolic demand. Lack of adaptation to these changes may lead to complications arising from hyperventilation or hypoventilation.

A novel adaptive closed-loop neuromorphic controller for respiratory pacing has been developed to address the need for closed-loop control respiratory pacing capable of responding to changes in metabolic production of CO2, diaphragm muscle health, and biomechanics. A 3-stage processes was utilized to develop the controller. First, an adaptive controller that could follow a preset within-breath volume profile was developed in silico and evaluated in vivo in anesthetized rats with an intact spinal cord or with diaphragm hemiparesis induced by spinal cord hemisection. Second, a neuromorphic computational model was developed to generate a desired trajectory that reflects changes in breath volume and respiratory rate in response to arterial CO2 levels. An enhanced controller capable of generating and matching this model-based desired trajectory was evaluated in silico and in vivo on rats with depressed ventilation and diaphragm hemiparesis. Finally, the enhanced adaptive controller was modified for human-related biomechanics and CO2 dynamics and evaluated in silico under changes of metabolic demand, presence of muscle fatigue, and after randomization of model parameters to reproduce expected between-subject differences. Results showed that the adaptive controller could adapt and modulate stimulation parameters and respiratory rate to follow a desired model-generated breath volume trajectory in response to dynamic arterial CO2 levels. In silico studies aimed at assessing potential for clinical translation showed that an enhanced controller modified for human use could successfully control ventilation to achieve and maintain normocapnic arterial CO2 levels. Overall, these results suggest that use of an adaptive closed-loop controller could lead to improved ventilatory outcomes and quality of life for users of adaptive respiratory pacing.

Identifier

FIDC007813

ORCID

https://orcid.org/0000-0002-3302-5496

COinS
 

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