DocumentCode :
87998
Title :
Prediction of Uterine Contractions Using Knowledge-Assisted Sequential Pattern Analysis
Author :
Zifang Huang ; Mei-Ling Shyu ; Tien, James M. ; Vigoda, Michael M. ; Birnbach, David J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Miami, Coral Gables, FL, USA
Volume :
60
Issue :
5
fYear :
2013
fDate :
May-13
Firstpage :
1290
Lastpage :
1297
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).
Keywords :
obstetrics; patient care; pattern recognition; support vector machines; analgesia efficacy; historical patient tracings; intrauterine pressure; knowledge assisted sequential pattern analysis; labor pain; medication; opioid remifentanil; optimal dosing regimen; uterine contraction; Association rules; Collaboration; Itemsets; Pain; Predictive models; Time series analysis; Training; Knowledge-based systems; pattern analysis; predictive models; support vector machine (SVM); uterine contraction; Databases, Factual; Female; Humans; Least-Squares Analysis; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Pregnancy; Support Vector Machines; Uterine Contraction;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2012.2232666
Filename :
6376142
Link To Document :
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