Abstract
In this paper we explore the use of updated tensor decompositions for the monitoring of brain hemodynamics in neonates. For this study, we used concomitant measurements of heart rate, mean arterial blood pressure, arterial oxygen saturation, EEG, and brain oxygenation - measured using near-infrared spectroscopy. These measurements were obtained from 22 neonates undergoing an INSURE procedure (INtubation, SURfactant and Extubation) and sedation using propofol. To develop the monitoring framework using tensors, we used radial basis kernel function (RBF) to construct a similarity matrix for consecutive segments of the signals. These matrices were concatenated forming a tensor. Updating canonical polyadic decomposition was used to evaluate the impact of propofol in the coupling between the different signals. Results indicate, as previously reported, a drop in the interaction between signals due to propofol administration. This shows that tensor decompositions can be useful in order to monitor the coupling between different physiological signals.
Original language | English |
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Title of host publication | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 660-663 |
Number of pages | 4 |
ISBN (Electronic) | 9781538613115 |
DOIs | |
Publication status | Published - Jul 2019 |
Event | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: 23 Jul 2019 → 27 Jul 2019 |
Publication series
Series | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
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ISSN | 1557-170X |
Conference
Conference | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
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Country/Territory | Germany |
City | Berlin |
Period | 23/07/19 → 27/07/19 |
Bibliographical note
*The research leading to these results has received funding from theEuropean Research Council under the European Union’s Seventh Framework
Programme (FP7/2007-2013) / ERC Advanced Grant: BIOTENSORS (no.
339804). This paper reflects only the authors’ views and the Union is not
liable for any use that may be made of the contained information.
Publisher Copyright: © 2019 IEEE.