Early warnings of heart rate deterioration


Hospitals can experience difficulty in detecting and responding to early signs of patient deterioration leading to late intensive care referrals, excess mortality and morbidity, and increased hospital costs. Our study aims to explore potential indicators of physiological deterioration by the analysis of vital-signs. The dataset used comprises heart rate (HR) measurements from MIMIC II waveform database, taken from six patients admitted to the Intensive Care Unit (ICU) and diagnosed with severe sepsis. Different indicators were considered: 1) generic early warning indicators used in ecosystems analysis (autocorrelation at-1-lag (ACF1), standard deviation (SD), skewness, kurtosis and heteroskedasticity) and 2) entropy analysis (kernel entropy and multi scale entropy). Our preliminary findings suggest that when a critical transition is approaching, the equilibrium state changes what is visible in the ACF1 and SD values, but also by the analysis of the entropy. Entropy allows to characterize the complexity of the time series during the hospital stay and can be used as an indicator of regime shifts in a patient’s condition. One of the main problems is its dependency of the scale used. Our results demonstrate that different entropy scales should be used depending of the level of entropy verified.

Publication DOI: https://doi.org/10.1109/EMBC.2016.7590856
Dataset DOI: https://doi.org/10.17036/ae7f18ce-a8d8-4c6d-a0b5-f5916dde49bc
Divisions: College of Engineering & Physical Sciences
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College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Event Type: Other
Event Dates: 2016-08-16 - 2016-08-20
Uncontrolled Keywords: Signal Processing,Biomedical Engineering,Computer Vision and Pattern Recognition,Health Informatics
ISBN: 978-1-4577-0220-4
Last Modified: 17 Jul 2024 07:21
Date Deposited: 01 Jun 2016 15:00
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2016-10-13
Accepted Date: 2016-05-07
Authors: Almeida, Vânia G. (ORCID Profile 0000-0002-2185-7850)
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)



Version: Accepted Version

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