Heart rate monitoring in neonatal intensive care using Markov models

Aleksandar Jeremić, Kenneth Tan

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

2 Citations (Scopus)


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
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)1424414849, 9781424414840
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)


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2008
Abbreviated titleICASSP 2008
Country/TerritoryUnited States of America
CityLas Vegas
Internet address


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

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