@inproceedings{b1e5016de63142cbbceb32e27066b326,
title = "Fetal heart rate classification by non-parametric Bayesian methods",
abstract = "In this paper, we propose an application of non-parametric Bayesian (NPB) models to classification of fetal heart rate recordings. More specifically, the models are used to discriminate between fetal heart rate recordings that belong to fetuses that may have adverse asphyxia outcomes and those that are considered normal. In our work we rely on models based on hierarchical Dirichlet processes. Two mixture models were inferred from recordings that represent healthy and unhealthy fetuses, respectively. The models were then used to classify new recordings. We compared the classification performance of the NPB models with that of support vector machines on real data and concluded that the NPB models achieved better performance.",
keywords = "classification, fetal heart rate, Gaussian mixture models, Hierarchical Dirichlet process, non-parametric",
author = "Kezi Yu and Quirk, \{J. Gerald\} and Djuric, \{Petar M.\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952281",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "876--880",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}