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Creating an Individualized Predictive Model of PAO2 and PACO2 Changes During Voluntary Static Apnea for Sedentary Subjects
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Att skapa en individualiserad prediktiv modell av PAO2- och PACO2-förändringar under frivillig statisk apné för stillasittande personer (Swedish)
Abstract [en]

The primary aim of this study was to fill a gap in the literature in understanding maximal BH in untrained, non-divers by generating a predictive numerical model for PAO2 and PACO2 throughout BH. There have been little to no previous attempts at explicitly characterizing the influence of impermanent personal or environmental factors on PAO2 or PACO2 at BH breakpoint. The metabolic human consumption of O2 and production of CO2 as measured through alveolar partial pressures was observed over time during a voluntary maximum breath-hold for 18 members of the general population. The coefficient of determination was used to determine accuracy of the model in fitting participants’ BH data from this experiment. The volume of the last inhalation pre-BH, time to subjective breakpoint, and breath-to-breath calculated respiratory exchange ratio (RER) were identified as the most influential combination of key variables that improved PAO2 model fit (from R2 = 0.8591 to R2 = 0.8840). Clustering methods coupled with two sample t-tests or ANOVA were then used to identify survey responses most correlated to inter-BH similarities. These were barometric pressure, age, height, weight, resting HR, smoker/ freediver/scuba experience, and weekly frequency of engaging in physical exercise. The model was validated on testing data from an experienced free-diver, from non-rebreathing trials of a sample of the participants, and from simulated dives of 5 participants from research in the Environmental Physiology Department of Karolinska in 1994 [1]. It has been suggested that the presented model can be a valuable tool in developing safer free diving practices. Furthermore, interesting trends in continuous HR, starting PACO2 values, and O2 consumption were observed and analyzed using statistical analysis. Findings were discussed with connection to the underlying physiological principles that might explain the results obtained.

Place, publisher, year, edition, pages
2018. , p. 70
Series
TRITA-CBH-GRU ; 2018:58
Keywords [en]
Breath holding, modeling, neural network
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-231337OAI: oai:DiVA.org:kth-231337DiVA, id: diva2:1224089
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2018-06-26Bibliographically approved

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