Development of a modular pulmonary resuscitation device for chronic and acute respiratory support

Noninvasive ventilation (NIV) and invasive ventilation are two distinct approaches to managing respiratory failure and supporting individuals with impaired breathing. These techniques are crucial in critical care medicine and respiratory therapy, offering life-saving interventions for various pulmonary disorders. Noninvasive ventilation represents a method of respiratory support that aids patients without requiring invasive procedures such as endotracheal intubation. It typically involves the use of a noninvasive ventilator or positive airway pressure devices to deliver air or oxygen through a mask or interface, aiding patients in maintaining adequate oxygenation and carbon dioxide removal. NIV has emerged as a preferred choice in various clinical scenarios, including chronic obstructive pulmonary disease (COPD) exacerbations, acute respiratory failure, congestive heart failure, and sleep-related breathing disorders. This approach offers several distinct advantages, such as reduced risk of ventilator-associated pneumonia, improved patient comfort, and the possibility of patient self-management, making it an increasingly valuable tool in respiratory medicine. The emergence of the COVID-19 pandemic in 2020 prompted the creation of numerous affordable, open-source, or readily constructed ventilators for urgent deployment. However, most of the proposed setups constituted of either automation of a hand-operated resuscitator called Bag Valve Mask (BVM), which is limited in its ability for pressure and volume control, or the development of pressurized valve-based mechanisms that require a constant high-pressure input source, making them redundant in scenarios where pressurized air/oxygen source is not available. Furthermore, these solutions are generally catered towards invasive and intubated mechanical ventilation techniques. Turbine-based positive pressure ventilators bridge the gap between portability and advanced therapy modes for pulmonary therapy via non-invasive ventilation. The existing body of literature exhibits a notable paucity of comprehensive examinations concerning the conception, evaluation, and viability of turbine-based non-invasive ventilators as an economically efficient and readily deployable platform for respiratory support. This deficiency in research extends to the exploration of their potential applications in contexts extending beyond emergency situations, particularly in the domain of home care ventilation. This thesis delves into examining and evaluating the creation of a modular respiratory system that can provide continuous positive pressure and Bilevel positive pressure therapy. In the first phase of this research study, a high-pressure turbine-based positive-pressure support, non-invasive ventilator design is proposed. The developed hardware can deliver multi-mode respiratory therapy, namely pressure support and volume-assured pressure support ventilation. The novel modular device works as a non-invasive respiratory support device with novel algorithms developed to maximize patient machine synchronization and robust closed-loop control strategy for pressure therapy during varying pulmonary compliance. In the second part of this research, a novel real-time wireless sensory module technique is presented for advanced respiratory monitoring and control during non-invasive ventilation. The proposed techniques focus on improving the current standards of monitoring and therapy in continuous positive pressure systems utilizing single-limb circuits with passive leak ports. Implementing the proposed wireless pressure and flow monitoring, in conjunction with the developed modular respiratory device, yields enhancements in the device's responsiveness in detecting distinct patient respiratory phases. Furthermore, this setup effectively alleviates the necessity for computational modeling of dynamic leakages, consequently facilitating the precise monitoring of inspiratory and expiratory tidal volumes in Non-Invasive Ventilation (NIV). Notably, these capabilities significantly advance compared to conventional homecare ventilatory support utilizing single-limb passive leak circuits. In the third part of this research, real-time assessment for pulmonary mechanics is performed via Machine learning techniques from data acquired in real-time during non-invasive pressure therapy mode. The pulmonary mechanics are key monitoring parameters for ensuring correct therapy mode and therapy pressure or volume levels are provided for patient care. To date, for pulmonary mechanic measurement, an inhalation pause maneuver is adapted. This maneuver is only possible during invasive mechanical ventilation, where patient-derived breathing is absent, and the total breathing is controlled via the ventilator. The absence of an inhalation pause maneuver or any active spirometry during non-invasive ventilation makes monitoring pulmonary mechanics extremely challenging. Thus we present a novel framework for the development of machine learning techniques that can predict pulmonary mechanics like compliance and resistance with an overall accuracy above 95% with and without system leakages, leading to a novel digital framework that allows for lung mechanics monitoring without clinical intervention and can help save ventilator induced lung injuries.