TY - GEN
T1 - Analysis of fetal heart rate series by nonparametric hidden Markov models
AU - Yu, Kezi
AU - Quirk, J. Gerald
AU - Djurić, Petar M.
N1 - Publisher Copyright: © 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Fetal heart rate (FHR) signals are routinely monitored to help obstetricians assess fetal status. In addition to guidelines for visual inspections, much research has been focused on computerized analysis of FHR tracings. In this paper, we propose to process FHR series by hidden Markov models (HMMs) and associate the hidden states with patterns of the tracings. Furthermore, we employ a nonparametric Bayesian approach, which does not define the number of hidden states before-hand, but instead uses data to determine the most appropriate number of states. We propose to use a nonparametric HMM, known as sticky hierarchical Dirichlet process-hidden Markov model (HDP-HMM) to resolve problems that arise due to redundant states and rapid switching rate of basic non-parametric models. We use the HDP-HMMs to classify FHR signals into two groups and compare the results with those of support vector machines (SVMs). The classification performance showed that the HMM-based method achieved better accuracy.
AB - Fetal heart rate (FHR) signals are routinely monitored to help obstetricians assess fetal status. In addition to guidelines for visual inspections, much research has been focused on computerized analysis of FHR tracings. In this paper, we propose to process FHR series by hidden Markov models (HMMs) and associate the hidden states with patterns of the tracings. Furthermore, we employ a nonparametric Bayesian approach, which does not define the number of hidden states before-hand, but instead uses data to determine the most appropriate number of states. We propose to use a nonparametric HMM, known as sticky hierarchical Dirichlet process-hidden Markov model (HDP-HMM) to resolve problems that arise due to redundant states and rapid switching rate of basic non-parametric models. We use the HDP-HMMs to classify FHR signals into two groups and compare the results with those of support vector machines (SVMs). The classification performance showed that the HMM-based method achieved better accuracy.
UR - https://www.scopus.com/pages/publications/85050997917
U2 - 10.1109/ACSSC.2017.8335567
DO - 10.1109/ACSSC.2017.8335567
M3 - Conference contribution
T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
SP - 1318
EP - 1322
BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
A2 - Matthews, Michael B.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Y2 - 29 October 2017 through 1 November 2017
ER -