Prediction of uterine contractions using knowledge-assisted sequential pattern analysis

Zifang Huang; Mei Ling Shyu; James M. Tien; Michael M. Vigoda; David J. Birnbach

doi:10.1109/TBME.2012.2232666

Prediction of uterine contractions using knowledge-assisted sequential pattern analysis

Zifang Huang, Mei Ling Shyu, James M. Tien, Michael M. Vigoda, David J. Birnbach

Research output: Contribution to journal › Article

10 Scopus citations

Abstract

The usage of the systemic opioid remifentanil in relieving the labor pain has attracted much attention recently. An optimal dosing regimen for administration of remifentanil during labor relies on anticipating the timing of uterine contractions. These predictions should be made early enough to maximize analgesia efficacy during contractions and minimize the impact of the medication between contractions. We have designed a knowledge-assisted sequential pattern analysis framework to 1) predict the intrauterine pressure in real time; 2) anticipate the next contraction; and 3) develop a sequential association rule mining approach to identify the patterns of the contractions from historical patient tracings (HT).

Original language	English (US)
Article number	6376142
Pages (from-to)	1290-1297
Number of pages	8
Journal	IEEE Transactions on Biomedical Engineering
Volume	60
Issue number	5
DOIs	https://doi.org/10.1109/TBME.2012.2232666
State	Published - May 1 2013

Keywords

Knowledge-based systems
pattern analysis
predictive models
support vector machine (SVM)
uterine contraction

ASJC Scopus subject areas

Biomedical Engineering

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10.1109/TBME.2012.2232666

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Huang, Z., Shyu, M. L., Tien, J. M., Vigoda, M. M., & Birnbach, D. J. (2013). Prediction of uterine contractions using knowledge-assisted sequential pattern analysis. IEEE Transactions on Biomedical Engineering, 60(5), 1290-1297. [6376142]. https://doi.org/10.1109/TBME.2012.2232666

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