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Machine Learning for Neonatal Early Warning Signs
KTH, School of Electrical Engineering (EES), Information Science and Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Cardio-respiratory dysfunction, sepsis and necrotizing enterocolitis are responsible for a large numberof deaths in the neonatal population. Despite ecient monitoring and screening in Intensive CareUnits, diagnosis prior to clinical symptoms remains a dicult task. Based on Heart Rate Monitoring,the state-of-the-art HeRO system indicates the risk for sepsis and has already proven its ability toreduce mortality in the neonatal ICU. Recent studies have shown that a particular respiratory behaviorknown as ABD-events, can be used as a physiomarker for sepsis and is therefore an early warningsign. Detecting ABD-events is currently done by simple thresholding techniques. Based on cardiorespiratorydata and hindsight from previous patients, we aim at improving the early warning systemby applying machine learning algorithms. Data with higher frequency than those used in the HeROsystem and biological samples are still to be collected, but still, using low frequency data, we managedto obtain a specicity (true positive) of 70% and a sensitivity (true negative) of 65% on manuallylabeled events. In this report, the theoretical framework is presented along with the practical issuesencountered during the project.

Abstract [sv]

Varje år dör många nyfödda barn i hjärtproblem, sepsis och nekrotiserande enterokolit. Att ställadiagnos innan kliniska symptom är uppenbara är fortfarande mycket svårt, trots effektiv övervakningoch screening inom intensivvården. Med hjälp av kontinuerlig hjärtövervakning med hjälp HeROsystemetkan kan risken för sepsis beräknas. Förekomsten av särskilda förändringar i barnets andningsmönster (apné, bradykardi och desaturation - ABD) kan användas som en tidig fysiomarkörför sepsis och fungerar därför som en varningssignal. I nyligen presenterade studier har detta visatsminska dödligheten på neontalavdelningar. Dessa ABD-händelser har fram till nu upptäckts genomenkel tröskelnivåbedömning. Baserat på hjärt- och andningsövervakningsdata och kunskap om tidigarepatienter, vill vi förbättra detta system för tidiga varningssignaler genom att använda maskininlärningsalgoritmer. Analys av högfrekvensdata och biomarkörer kvarstår att göra, men ävenbaserat på lågfrekvensdata kunde vi uppnå en specificitet på 70% och en sensitivitet på 65%. Dennarapport sammanfattar den teoretiska bakgrunden till analysmetoden och diskuterar praktiska frågorsom identiferats under arbetets gång.

Place, publisher, year, edition, pages
2017. , p. 28
Series
TRITA-EE, ISSN 1653-5146 ; 2017:043
Keywords [en]
ABD-events, Machine learning, Oversampling
Keywords [sv]
ABD-händelser, maskininlärning, Oversampling
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-208996OAI: oai:DiVA.org:kth-208996DiVA, id: diva2:1109509
External cooperation
Astrid Lindgrens Barnsjukus
Educational program
Master of Science in Engineering - Electrical Engineering
Supervisors
Available from: 2017-06-14 Created: 2017-06-14 Last updated: 2017-06-14Bibliographically approved

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