Objective

Methods

Results

Conclusion

Significance

Over 1 million patients are implanted annually with a pacemaker for the treatment of cardiac arrhythmias and conduction disorders. However, despite numerous advancements in pacemaker technology, lead-related complications associated with these devices continue to compromise patient safety and survival. Wireless power transfer holds great promise for improving health outcomes in biomedical implants such as the pacemaker. However, efficient power transfer and effective operational range have remained a challenge within anatomical constraints. In this work, we demonstrated an intravascular, wirelessly-powered, batteryless, microscale pacer deployed to the anterior cardiac vein (ACV). First, we employed a novel intermittent transmission remote-control architecture with improved power efficiency to enable sufficient power transfer without compromising on Specific Absorption Rate (SAR). We then integrated this architecture into a 3-tiered, 2-sub-system, 4-coil design, which operates on two different frequencies. Our pacemaker was designed to achieve wireless power transfer over a 55 mm range from an external transmitter to an intravascular receiver. A charging sub-system was designed to transmit power over 25 mm using inductive power transfer to a subcutaneous receiver, which then charged a battery feeding the transmitter of a stimulation sub-system. We then introduced a stimulator circumferentially confined to a 3 mm diameter hollow-cylinder that delivered >5 V using wireless power transfer at 13.56 MHz, with over 20 mm transmitter-receiver displacement between the subcutaneous unit and pacer unit. Further validation was performed using Finite Element Method (FEM) simulation of the cardiac cycle, guided by anatomical variations established by Magnetic Resonance Images (MRI). Finally, we demonstrated the capacity for both ex vivo and in vivo pacing of pig hearts following pacer deployment in the ACV. This introduced, for the first time, the unprecedented capacity for wireless intravascular pacing with potential for multi-organ stimulation. Thus, the proposed system design has the potential to bypass the multitude of complications associated with pacemaker wires, repeated procedures for battery replacement, and mechanically burdened fixation mechanisms.

This proposal encompasses a comprehensive investigation into non-invasive cardiovascular monitoring techniques, which involved developing and integrating novel signal processing algorithms and machine learning (ML) architectures. We introduced a unique algorithm employing a novel intra-beat biomarker, designed for correcting raw BP data from various sensor sources. Our diastolic transit time (DTT)-based approach successfully eliminated stochastic baseline wander, enabling accurate, single-sensor beat-to-beat blood pressure (BP) measurements. This method not only maintained the spatiotemporal integrity of signals but also mitigated motion artifacts, paving the way for its application in continuous non-invasive BP technologies for acute, outpatient, and home settings. Our investigations into beat-to-beat heart rate and BP variability revealed significant physiological insights, particularly in stroke and sickle cell disease (SCD) patients. We observed increased systolic BP variability in stroke patients compared to healthy controls, a finding that underscores the potential of BP variability as a marker for long-term monitoring and management. In SCD patients, our study highlighted the impact of mental and psychological stressors on BP variability and vascular tone, revealing a complex interplay between autonomic nervous system dysregulation, endothelial dysfunction, and vascular tone alterations. These insights suggest the need for holistic management strategies that address both physiological and psychosocial aspects of disease. The application of ML in predictive health analytics marks a significant advancement in non-invasive monitoring of cardiac output (CO) and intracranial pressure (ICP). We developed a model for real-time, accurate CO monitoring using arterial line BP data, demonstrating the potential of ML to revolutionize acute care patient management. Additionally, our work in developing a non-invasive sensing system for continuous ICP measurement through beat-to-beat BP correlation offers a promising avenue for enhancing neurologic patient care, potentially reducing patient morbidity, mortality, and cost burden. Collectively, this research not only advances our understanding of cardiovascular physiology and its implications for patient care but also showcases the potential of integrating wearable technology and predictive analytics into clinical practice. Thus, the findings and methodologies presented in this dissertation offer significant implications for the development of non-invasive monitoring technologies, with the potential to improve patient outcomes across a broad spectrum of clinical settings.

The advent of minimally invasive surgery (MIS) led to significant benefits for patients at a cost of increase technical difficulty for surgeons. Robotic minimally invasive surgery (RMIS) was introduced to help eliminate some of the outstanding challenges by introducing improvements such as enhanced 3D vision and additional degrees of freedom. Unfortunately, RMIS resulted in a complete loss of haptic feedback, a problem that has persisted even after more than a decade of technology development.

The limitations introduced by the loss of feedback in robotic surgery gave birth to innovations and significant research on haptic feedback systems (HFS). These systems aimed to provide an artificial sense of touch. Researchers have focused on many varieties of feedback technologies, most often relying on one specific feedback modality to help improve performance in a few, limited robotic surgical procedures.

This research project set out to investigate multi-modal haptic feedback systems capable of providing benefits for many different robotic surgical applications. Having inherited an existing tactile feedback system designed for reducing crush injuries in robotic surgical procedures, this project implemented various critical enhancements for pneumatic normal force tactile feedback. Improvements to the sensing technology such as design of shear sensing mechanisms helped expand the application of haptics beyond grip force reduction.

The development and integration of additional modalities of feedback including kinesthetic force feedback and vibration feedback, and design of a highly configurable software architecture allowed the application of the multi-modal HFS in several different RMIS applications. Evaluation of the system for knot tying in robotic surgery showed significant benefits in reducing suture breakage and improving knot quality. Application of the multi-modal HFS for palpation in robotic surgery helped improve detection non-compressible structures such as tumors and vessels in soft tissue phantoms. Finally, the system improved upon the previously developed unimodal tactile feedback systems with regards to reduction of grip force in RMIS.

The results of these investigations highlight the importance of developing multi-modal haptic feedback systems that are able simulate the synergistic relationship between the various feedback modalities involved in real human touch. Robotic surgical systems have long been held back by their lack of comprehensive haptic feedback solutions. Multi-modal haptic feedback systems hold the promise of eliminating this long-standing problem and helping expand the application of robotics in surgical sciences.