Neonatal heart rate prediction

Yumna Abdel-Rahman, Aleksander Jeremic, Kenneth Tan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Technological advances have caused a decrease in the number of infant deaths. Pre-term infants now have a substantially increased chance of survival. One of the mechanisms that is vital to saving the lives of these infants is continuous monitoring and early diagnosis. With continuous monitoring huge amounts of data are collected with so much information embedded in them. By using statistical analysis this information can be extracted and used to aid diagnosis and to understand development. In this study we have a large dataset containing over 180 pre-term infants whose heart rates were recorded over the length of their stay in the Neonatal Intensive Care Unit (NICU). We test two types of models, empirical bayesian and autoregressive moving average. We then attempt to predict future values. The autoregressive moving average model showed better results but required more computation.

Original languageEnglish
Title of host publicationProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
EditorsXiaochuan Pan, Gregory Worrell
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4695-4698
Number of pages4
ISBN (Print)9781424432967
DOIs
Publication statusPublished - 2009
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2009 - The Hilton Minneapolis, Minneapolis, United States of America
Duration: 3 Sep 20096 Sep 2009
Conference number: 31

Conference

ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2009
Abbreviated titleEMBC 2009
CountryUnited States of America
CityMinneapolis
Period3/09/096/09/09

Cite this

Abdel-Rahman, Y., Jeremic, A., & Tan, K. (2009). Neonatal heart rate prediction. In X. Pan, & G. Worrell (Eds.), Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (pp. 4695-4698). [5334205] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IEMBS.2009.5334205
Abdel-Rahman, Yumna ; Jeremic, Aleksander ; Tan, Kenneth. / Neonatal heart rate prediction. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. editor / Xiaochuan Pan ; Gregory Worrell. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2009. pp. 4695-4698
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Abdel-Rahman, Y, Jeremic, A & Tan, K 2009, Neonatal heart rate prediction. in X Pan & G Worrell (eds), Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009., 5334205, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 4695-4698, International Conference of the IEEE Engineering in Medicine and Biology Society 2009, Minneapolis, United States of America, 3/09/09. https://doi.org/10.1109/IEMBS.2009.5334205

Neonatal heart rate prediction. / Abdel-Rahman, Yumna; Jeremic, Aleksander; Tan, Kenneth.

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. ed. / Xiaochuan Pan; Gregory Worrell. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2009. p. 4695-4698 5334205.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AB - Technological advances have caused a decrease in the number of infant deaths. Pre-term infants now have a substantially increased chance of survival. One of the mechanisms that is vital to saving the lives of these infants is continuous monitoring and early diagnosis. With continuous monitoring huge amounts of data are collected with so much information embedded in them. By using statistical analysis this information can be extracted and used to aid diagnosis and to understand development. In this study we have a large dataset containing over 180 pre-term infants whose heart rates were recorded over the length of their stay in the Neonatal Intensive Care Unit (NICU). We test two types of models, empirical bayesian and autoregressive moving average. We then attempt to predict future values. The autoregressive moving average model showed better results but required more computation.

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Abdel-Rahman Y, Jeremic A, Tan K. Neonatal heart rate prediction. In Pan X, Worrell G, editors, Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2009. p. 4695-4698. 5334205 https://doi.org/10.1109/IEMBS.2009.5334205