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Extraction of Heart Rate from Multimodal Video Streams of Neonates using Methods of Machine Learning
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The World Health Organization estimates that more than one-tenth of births are premature.
Premature births are linked to an increase of the mortality risk, when compared with
full-term infants. In fact, preterm birth complications are the leading cause of perinatal
mortality. These complications range from respiratory distress to cardiovascular disorders.
Vital signs changes are often prior to these major complications, therefore it is crucial to perform
continuous monitoring of this signals. Heart rate monitoring is particularly important.
Nowadays, the standard method to monitor this vital sign requires adhesive electrodes or
sensors that are attached to the infant. This contact-based methods can damage the skin
of the infant, possibly leading to infections. Within this context, there is a need to evolve to
remote heart rate monitoring methods.
This thesis introduces a new method for region of interest selection to improve remote
heart rate monitoring in neonatology through Photoplethysmography Imaging. The heart
rate assessment is based on the standard photoplethysmography principle, which makes use
of the subtle fluctuations of visible or infrared light that is reflected from the skin surface
within the cardiac cycle. A camera is used, instead of the contact-based sensors. Specifically,
this thesis presents an alternative method to manual region of interest selection using
methods of Machine Learning, aiming to improve the robustness of Photoplethysmography
Imaging. This method comprises a highly efficient Fully Convolutional Neural Network to
select six different body regions, within each video frame. The developed neural network
was built upon a ResNet network and a custom upsampling network. Additionally, a new
post-processing method was developed to refine the body segmentation results, using a
sequence of morphological operations and centre of mass analysis. The developed region of
interest selection method was validated with clinical data, demonstrating a good agreement
(78%) between the estimated heart rate and the reference.
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photoplethysmographic imaging heart rate monitoring premature infant deep learning convolutional neural network image semantic segmentation