Due to copyright issues, an electronic copy of the thesis must be ordered from the faculty. For the faculty to have time to process the order, the order must be received by the faculty at the latest 2 days before the public defence. Orders received later than 2 days before the defence will not be processed. After the public defence, please address any inquiries regarding the thesis to the candidate.
Trial Lecture – time and place
See Trial Lecture.
Adjudication committee
- First opponent: Associate Professor Grégoire Emmanuel Würzner, Université de Lausanne, Switzerland
- Second opponent: Senior Researcher Tine Willum Hansen, Steno Diabetes Center Copenhagen, Denmark
- Third member and chair of the evaluation committee: Associate Professor Jørgen Gravning, University of Oslo
Chair of the Defence
Professor Emeritus Dag Jacobsen, University of Oslo
Principal Supervisor
Senior Consultant Bård Endre Waldum-Grevbo, Oslo University Hospital
Summary
Cuff measurements remain the gold standard for blood pressure (BP) measurements. However, it has hardly evolved for over a century and can only provide snap-shot measurements which many find uncomfortable. In recent years, cuffless BP using wearable devices has emerged. The aim of the thesis was to investigate whether non-invasive measurements of pulse arrival time (PAT), which has a theoretical relationship with BP, derived from ECG and PPG signals from a chest belt (Cuffless Device) could track changes in BP. We aimed to develop a model based on PAT and compare it to cuff 24-hour ambulatory BP measurements (24ABPM) and intra-arterial BP in intensive care patients.
In paper I and II we investigated the relationship between changes in PAT and BP during exercise. We found strong correlations between PAT and systolic BP, but uncertain correlations between PAT and diastolic BP. Based on these results we developed a pilot model and hypothesized that it could track changes during 24ABPM. However, the model consistently over estimated BP in 95 participants during 24ABPM and showed no correlation with the cuff measurements. We also investigated discomfort using a questionnaire, in which the participants favored the Cuffless Device. In paper IV, the PAT-based model was compared to intra-arterial BP in 25 intensive care unit patients. We further hypothesized that more complex machine learning models based on the PPG signal could improve the results. The PAT-based model was not able to track changes in BP compared to intra-arterial BP. However, more complex machine learning based models improved the results.
In conclusion, PAT is not a feasible method to enable non-invasive cuffless BP measurements. This is likely due to too many confounding factors other than BP which affects PAT. Cuffless BP based on signal analysis of a PPG signal showed promise and is being further investigated.
Additional information
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