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Pulse Oximetry: Signal Processing in real time on Raspberry Pi
KTH, School of Technology and Health (STH).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Pulsoximetri : Signalbehandling i realtid på Raspberry Pi (Swedish)
Abstract [en]

This thesis introduces the reader into RespiHeart, which is a product under development. RespiHeart is an complement/alternative to the regular Pulse Oximeter and is intended to be used within the health care sector for combined measurements and communication on open inexpensive platforms.

This thesis evaluates interaction between RespiHeart and a Raspberry Pi 3 Model B to evaluate if the computer is capable of handling the data from RespiHeart and make signal processing.

Python is used throughout the whole project and is a suitable language for interaction and signal processing in real time.

Abstract [sv]

Detta examensarbete introducerar läsaren till RespiHeart, en ny trådlös produkt som är under utveckling. RespiHeart är ett komplement/alternativ till den nuvarande Pulsoximetern och är tänkt att användas inom sjukvården för analys, kommuniakation och kombinerade mätningar på öppna billiga plattformar.

Detta project utvärderar interaktionen mellan RespiHeart och en Raspberry Pi 3 Model B för att undersöka om datorn är kapabel till att hantera datan från RespiHeart samt göra signal processing i real tid.

Programmeringsspråket Python användes under hela projektet och är ett lämpligt språk att använda för interation och signal processing i real tid.

Place, publisher, year, edition, pages
2017. , p. 39
Series
TRITA-STH ; 2017:68
Keywords [en]
Pulse Oximetry, Raspberry Pi, Signal processing, Filtering, RespiHeart, Healthcare, Python, FIR, IIR, Filter, IMSE RToS, Matlab, Electronic Health Record, Real Time
Keywords [sv]
Pulsoximetri, Raspberry Pi, Signalbehandling, Filtrering, RespiHeart, Sjukvård, Python, FIR, IIR, Filter, IMSE RToS, Matlab, Elektronisk Patient Journal, Realtid
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-210234OAI: oai:DiVA.org:kth-210234DiVA, id: diva2:1117776
Subject / course
Medical Engineering
Educational program
Master of Science in Engineering - Medical Engineering
Supervisors
Examiners
Available from: 2017-06-29 Created: 2017-06-29 Last updated: 2017-06-29Bibliographically approved

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