1. Development of a System to Evaluate the Health Impacts of Beddings and Sleeping Environments for Newborns
Mattresses and beds for children can pose potential health risks to infants and young children, such as airway obstruction, the creation of confined spaces with insufficient air circulation leading to the re-inhalation of exhaled CO2, and the development of pressure sores on the head, particularly in neonatal intensive care units, where newborns may be immobilized by ventilation equipment. Various factors, including mattress materials, shapes, and environmental conditions, can either contribute to or mitigate these issues. Therefore, it is essential to develop a system capable of assessing the impact of mattresses and bedding on children’s health by accurately replicating the interaction between these elements. Currently, a testing setup is available that includes 3D-printed infant heads derived from MRI scans, a neonatal breathing simulator, and a series of sensors for monitoring respiratory flow, CO2 exchanged with the environment and contact forces. However, further advancements are needed to enhance the system’s capabilities. These include adding new sensors, optimizing existing code by centralizing it on a single microcontroller, developing graphical interfaces for monitoring all measured variables, and designing sensorized heads equipped with pressure, temperature, and humidity sensor arrays to assess these variables at the mattress interface. Students undertaking this thesis project will engage in activities such as studying sensor datasheets, writing firmware for sensor management, developing graphical interfaces for data visualization, designing electronic circuits and performing soldering, designing chambers and connectors using CAD software with 3D printing, integrating actuators like heaters and humidifiers with appropriate control systems, and conducting measurements with the developed system to evaluate mattresses.
The expected duration of the thesis is between 10 and 14 months, depending on the student’s level of commitment and the inherent unpredictability of experimental work. Students will be required to carry out most of their activities in the laboratory, as these tasks necessitate the use of equipment and hardware available only on-site. Additionally, working in the laboratory will enable them to interact directly with tutors and colleagues, fostering collaboration and teamwork.
For further information about this project, please contact: chiara.veneroni@polimi.it, matteo2.mentasti@mail.polimi.it.
2. Development of a Mechanical Ventilation System Based on Surface Electromyography of the Diaphragm for Preterm Infants
Prematurity, defined as birth before the 37th week of gestation, is a critical issue in neonatal health. Being born too early disrupts the normal maturation of vital organs, particularly the lungs, often leading to severe complications. Preterm infants face a much higher risk of respiratory problems compared to full-term infants. These include conditions like Infant Respiratory Distress Syndrome (iRDS) and Bronchopulmonary Dysplasia (BPD), which frequently require mechanical ventilation (MV) to support breathing. For preterm infants, MV must not only provide effective respiratory support but also synchronize with the infant’s spontaneous breathing to avoid unnecessary strain and complications. Most ventilators currently rely on flow and pressure sensors to detect the infant’s respiratory effort. However, these methods can be inaccurate due to factors like air leaks in the circuit or changes in the mechanical properties of the infant’s lungs. To address these issues, researchers in Toronto developed Neurally Adjusted Ventilator Assist (NAVA). NAVA uses signals from the diaphragm, the primary respiratory muscle, to control the ventilator. These signals are captured via a special esophageal catheter equipped with electrodes. While NAVA improves synchronization significantly, it has drawbacks: the catheter is invasive, expensive, works only with specific ventilators, and increases the risk of infection.
This raises a key research question: how can we achieve the precision of NAVA while eliminating its invasiveness? This thesis explores the use of surface electromyography of the diaphragm as a non-invasive alternative for detecting spontaneous breathing in preterm infants. The objective is to develop and optimize a real-time system that uses surface electromyography signals to trigger mechanical ventilation, leveraging machine learning or traditional signal processing methods.
The thesis workflow includes four main steps: developing a real-time algorithm for signal processing, implementing basic hardware to run the algorithm in real-time, validating the system in vitro at Techres Lab, Politecnico di Milano, conducting a pilot clinical study in collaboration with Amsterdam UMC. The expected duration of the thesis is between 10 and 14 months. Laboratory attendance is strongly recommended. Additionally, there may be opportunities to travel for short to medium periods as part of project activities or collaborations.
For further information, please contact Ilaria Girimonte at ilaria.girimonte@polimi.it.
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