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 language | English |
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Title of host publication | 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP |
Pages | 485-488 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2008 |
Externally published | Yes |
Event | IEEE International Conference on Acoustics, Speech and Signal Processing 2008 - Las Vegas, United States of America Duration: 31 Mar 2008 → 4 Apr 2008 https://ieeexplore.ieee.org/xpl/conhome/4505270/proceeding (Proceedings) |
Conference
Conference | IEEE International Conference on Acoustics, Speech and Signal Processing 2008 |
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Abbreviated title | ICASSP 2008 |
Country | United States of America |
City | Las Vegas |
Period | 31/03/08 → 4/04/08 |
Internet address |
Keywords
- Heart-rate prediction
- Markov chains
- Neonatal intensive care