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Prototype algorithm development for innovative sensor technology
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Preterm infants are sensitive to incorrect levels of oxygen in the blood. Thus, the oxygen levels need to be monitored continuously. The most commonly used method today has several limitations and therefore constant blood tests has to be taken.

Neosense Technologies AB has developed an electrochemical sensor that measures blood oxygen tension continuously and in real time.

This thesis aims to develop a prototype algorithm to derive cardiac output based on dynamic changes in inhaled fraction of oxygen and the corresponding change in arterial oxygen tension using the electrochemical sensor.

Two measurements were done using the sensor to measure the step response of partial pressure of oxygen to obtain data to develop the algorithm. Using the obtained data, two algorithms were developed and from the validation analysis one of the algorithms were chosen due to significant better results.

Abstract [sv]

För tidigt födda barn är känsliga för felaktiga syrgasnivåer i blodet. Därför krävs det noggranna och kontinuerliga mätningar. Dagens metoder är begränsade och ständiga blodprover behöver tas.Neosense Technologies AB har utvecklat en sensor som mäter syrehalten i blodet kontinuerligt och i realtid.Syftet med detta examensarete är att utveckla en prototypalgoritm för att härleda hjärtminutvolymen baserat på dynamiska förändringar i syrehalten i inandningsluften och den motsvarande syrgasnivån i blodet med hjälp av den elektrokemiska sensorn.Två mätningar gjordes med hjälp av sensorn där stegsvaret för partialtrycket av syre mättes för att erhålla data för algoritmutvecklingen. Med hjälp av den erhållna datan utvecklades två algoritmer och från valideringsanalysen valdes en av algoritmerna på grund av betydande bättre resultat.

Place, publisher, year, edition, pages
2018. , p. 36
Series
TRITA-EECS-EX ; 2018:15
National Category
Embedded Systems Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-239443OAI: oai:DiVA.org:kth-239443DiVA, id: diva2:1265408
External cooperation
Neosense Technologies AB
Subject / course
Computer Science
Educational program
Master of Science - Embedded Systems
Supervisors
Examiners
Available from: 2018-11-23 Created: 2018-11-23 Last updated: 2018-11-23Bibliographically approved

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School of Electrical Engineering and Computer Science (EECS)
Embedded SystemsComputer and Information Sciences

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CiteExportLink to record
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Citation style
  • apa
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