Abstract:
Gastrointestinal (GI) motility is critical for efficient digestion and nutrient absorption, and is
coordinated in part by bio-electrical events known as slow waves. Severe motility disorders
such as gastroparesis and chronic nausea and vomiting, can be significantly detrimental to
quality of life. Pacing, or long pulse gastric electrical stimulation, is a potential therapy for
treating GI motility disorders by modulating the slow wave activity. Open-loop pacing of
the GI tract is the current standard for modulating dysrhythmic patterns, but is known to be
suboptimal and inefficient. Understanding the slow wave propagation patterns of Interstitial
Cells of Cajal (ICC) is essential when designing Gastric Electrical Stimulators (GESs) to
treat motility disorders. A GES with the ability to both sense and pace, working in closedloop
with the ICC, will enable efficient modulation of GI dysrhythmias. However, there is no
current existing GES that can sense, process, and actuate the ICC network in a closed-loop
manner. In addition, GESs should be validated and verified in closed-loop like other safetycritical
devices such as the cardiac pacemakers, before implantation. A major bottleneck in
GESs validation and verification is the unavailability of high-fidelity yet scalable GI models,
which can be adjoined in closed-loop with a GES for validation and verification.
Efficient and accurate organ models are crucial for closed-loop design and validation of
implantable medical devices. To this end, bio-electric slow wave modelling of the stomach
is investigated in this thesis. In particular, this thesis considers high-fidelity, scalable, and
efficient modelling of the natural pacemaker, ICC, based on the formal Hybrid Input Output
Automata (HIOA) framework. Our proposed model is founded on formal methods, a
collection of mathematically sound techniques originating in computer science for the design
and validation of safety-critical systems. Each ICC cell is modelled using a HIOA.
A HIOA path model is also introduced to capture the electrical propagation delay between
cells in a network. The simulated slow wave of a single ICC cell had a high correlation (≈
0.9) with the corresponding biophysical models. The proposed HIOA model is significantly
more efficient (with 9× to 2500× speedup, depending on the Ordinary Differential Equation
(ODE) solver) than the corresponding biophysical models and scales to larger networks
of ICC. The resultant network of cells can simulate normal and diseased action potential
propagation patterns akin to those observed during GI dysrhythmias, making it useful for device design and validation.
Moreover, this thesis leverages the proposed high-fidelity ICC network model to design
a closed-loop GES software, which can be applied in an experimental and clinical setting.
Several normal and dysrhythmic patterns observed in experimental recordings of patients
suffering from GI tract diseases were reproduced by the ICC network model. Capturing
intracellular potentials in an in-vivo setting is not viable, and therefore an extracellular potential
generation model is developed as a stepping stone to implement closed-loop control
by the GES. The activation patterns of the ICC network are captured by the extracellular
potential generation model and is integrated with the GES in a closed-loop to validate the
efficacy of the developed pacing algorithms. The proposed GES pacing algorithms extend
existing offline filtering and activation detection methods to process the sensed extracellular
potentials in real time. The GES algorithm detects bradygastric rhythms based on the sensing
results and actuates the ICC network via pacing to rectify dysrhythmic patterns. The
efficacy of the GES is validated by integrating it in closed-loop with the ICC network. Results
show that the proposed GES is able to sense the propagation patterns and modulate the
dysrhythmic patterns of bradygastria back to their normal state automatically. The proposed
design of the GES is flexible enough to treat a variety of diseased dysrhythmic patterns effectively
in a closed-loop. The work presented in this thesis has the potential to immensely
benefit patients suffering from GI motility disorders.