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 |
|---|---|
| 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, USA, Las Vegas, NV, United States of America Duration: 31 Mar 2008 → 4 Apr 2008 |
Conference
| Conference | IEEE International Conference on Acoustics, Speech and Signal Processing 2008 |
|---|---|
| Country | United States of America |
| City | Las Vegas, NV |
| Period | 31/03/08 → 4/04/08 |
Keywords
- Heart-rate prediction
- Markov chains
- Neonatal intensive care
Cite this
}
Heart rate monitoring in neonatal intensive care using Markov models. / Jeremić, Aleksandar; Tan, Kenneth.
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. 2008. p. 485-488 4517652.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Heart rate monitoring in neonatal intensive care using Markov models
AU - Jeremić, Aleksandar
AU - Tan, Kenneth
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Heart-rate prediction
KW - Markov chains
KW - Neonatal intensive care
UR - http://www.scopus.com/inward/record.url?scp=51449092354&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2008.4517652
DO - 10.1109/ICASSP.2008.4517652
M3 - Conference Paper
AN - SCOPUS:51449092354
SN - 1424414849
SN - 9781424414840
SP - 485
EP - 488
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
ER -