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TY  - THES
AU  - Lyra, Simon Alexander
TI  - Camera-based vital signs monitoring of neonates in real-time using deep learning
VL  - 79
PB  - RWTH Aachen University
VL  - Dissertation
CY  - Düren
M1  - RWTH-2025-04894
SN  - 978-3-8191-0119-9
T2  - Aachener Beiträge zur Medizintechnik
SP  - XXI, 209 Seiten : Illustrationen
PY  - 2025
N1  - Dissertation, RWTH Aachen University, 2025
AB  - Almost one in ten children worldwide is born prematurely. Due to their immature physiology, these newborns require extensive medical care and constant monitoring to support the development process and minimize associated complications. Today’s state-of-the-art monitoring techniques include wired sensors and electrodes attached to the patient’s bare skin with medical-grade adhesives. While close monitoring of vital signs is a key factor in neonatal care to intervene in pathological progression, the adhesives pose a significant risk to the immature and therefore vulnerable skin, as they can cause injuries that can lead to life-threatening infections. Thus, alternative techniques for continuous monitoring of neonatal patients that allow unobtrusive remote assessment of vital signs have the potential to replace wired sensors and improve the overall outcome of neonatal care by minimizing the risk of skin injury. One technology that is increasingly being investigated to meet these requirements is camera-based sensors that can measure vital signs in real-time without direct skin contact. This thesis presents novel approaches to multi-modal camera-based monitoring of vital signs in preterm infants with the aim of improving neonatal care. Three different software pipelines have been implemented using real-time image processing techniques from the rapidly advancing and innovative field of Deep Learning. These tools were validated by analyzing multi-modal image data from neonatal patients acquired during two clinical studies and one laboratory study using a novel phantom implemented with hardware components to simulate vital signs. The two clinical trials recorded a total of 33 subjects in neonatal intensive care units in India and Germany. The data was processed to extract cardiac activity, respiration, thermoregulation, and patient movement. Each study was conducted using a specially designed camera system, including an RGB camera and Infrared Thermography equipment for thermal imaging. The neonatal phantom was manufactured using rapid prototyping and used to validate a low-cost camera setup by simulating pathological conditions of cardiac and thermoregulatory processes monitored in real-time. To address the challenge of the limited availability of public medical image data, which is crucial for training data-driven prediction models, a novel approach for Deep Learning-based data augmentation was applied to the clinic-recorded data. The results presented demonstrate the potential of Deep Learning-based image processing for real-time monitoring of vital signs in neonates and show the possibilities of using hardware phantoms for laboratory studies, reducing expensive and time-consuming clinical trials. Finally, this work represents a step towards replacing potentially harmful wired sensors in neonatal care.
LB  - PUB:(DE-HGF)11 ; PUB:(DE-HGF)3
UR  - https://publications.rwth-aachen.de/record/1012152
ER  -