Aurora Cavallaro
Enhancing SpO2 Estimation through Deep Learning Approaches in Photoplethysmography.
Rel. Gabriella Olmo, Nicola Picozzi. Politecnico di Torino, Master of science program in Biomedical Engineering, 2024
Abstract
In an increasingly health-conscious world, the need for accurate and non-invasive monitoring of vital signs has led to remarkable advancements in medical technology. Among these innovations, photoplethysmography (PPG) stands out as a transformative method for assessing physiological parameters, particularly peripheral oxygen saturation (SpO2), which is vital for diagnosing and managing various health conditions such as respiratory and cardiovascular diseases. This thesis delves into the dynamic interplay between technology and biology, starting with a comprehensive investigation of the cardiovascular system and the underlying principles of PPG. It provides a foundation for understanding how light absorption may vary with blood flow and oxygenation and which could be the critical aspects of pulse oximetry, including wavelength selection and sensing modes.
A key focus of this work is the potential of neural networks to improve the accuracy of SpO2 estimation
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