Heart rate monitoring in neonatal intensive care using Markov models

Aleksandar Jeremić, Kenneth Tan

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Abstract

Although heart-rate is commonly measured in various clinical settings the advanced algorithms for its prediction are rarely implemented in clinical settings and patient management. In neonatal intensive care timely prediction of dangerous levels of heart rate can lead to improved care, long-term effects and reduced morbidity. In this paper we propose to model the heart-rate using Markov chain model and estimate transition probabilities using maximum likelihood estimator and the patient population from Neonatal Intensive Care Unit at McMaster Hospital. The probabilities of reaching high-risk states in predetermined time intervals are computed and the results are evaluated using the real data set.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages485-488
Number of pages4
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventIEEE International Conference on Acoustics, Speech and Signal Processing 2008 - Las Vegas, United States of America
Duration: 31 Mar 20084 Apr 2008
https://ieeexplore.ieee.org/xpl/conhome/4505270/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2008
Abbreviated titleICASSP 2008
CountryUnited States of America
CityLas Vegas
Period31/03/084/04/08
Internet address

Keywords

  • Heart-rate prediction
  • Markov chains
  • Neonatal intensive care

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