This document describes research on developing an automated system for analyzing uterine contractions to detect false labor using contraction data from pregnant women. The proposed approach involves preprocessing contraction signals, extracting morphological features, and using a multistage classification approach with a rule-based cascaded k-nearest neighbor classifier. Testing on 39 contraction traces found that the features of contraction intensity and time to peak classification performed best at distinguishing true labor from false labor.

1. Automated uterine contraction analysis for detecting false labor contractions Sujitkumar Hiwale, V. Pallavi, Celine Firtion, Rajendra Singh Sisodia Philips Research Asia - Bangalore, India 23 March, 2010

2. 2 Characteristics of true and false labor

3. 3 Problem description Aim of this work: Automated uterine contraction analysis for the detection of early and false labor Collaboration: Diwakar’sGlobal hospital, Bangalore Subjects: Pregnant women with gestational age between 36 to 40 weeks Data: 39 CTG traces (early labor: 20, false labor: 19) acquired from Philips Avalon FM 30 fetal monitor

5. Remove :

6. Textual information of the patient

7. Grids of the CTG graph

8. Sharp spikes in the signals due to the patient movements

11. Rule based cascaded k-nearest neighbor (KNN) classifier

12. All combination of independent features are experimented :

13. Best classification is with Time to peak from starting point of contraction (T2P) and Contraction Intensity (CI)True labor False labor

16. Lower value of T2P indicates a rapid propagation of contraction, and probably a more efficient type of contraction (one which leads to delivery)

17. In false labor this rate is slower than in true laborUI of the proposed system