Identification of preterm birth based on RQA analysis of electrohysterograms

Abstract

Background and objective: Common methods for data analysis are mainly based on linear concepts, but in recent years nonlinear dynamics methods have been introduced. It is a well-known fact that In typical biological systems lack of stationarity and rather sudden changes of state are the properties distinguishing them from each other. There is an urgent need to better understand the mechanical activity of the myometrium (its contractility) to find a solution for preterm delivery problem, the largest cause of neonatal deaths and morbidity. The electrohysterographic signal (EHG) is a good non-linear, bioelectrical indicator for the detection and identification of term and preterm birth.

Methods: The material of the study consists of EHG signals, obtained from 20 patients between the 24th and the 28th week of pregnancy with threatened preterm labor. The women were divided into two groups: those delivering after more than 7 days - group A (n = 10) and women delivering within 7 days - group B (n = 10). In this paper, an analysis of bioelectrical signals was performed by recurrence quantification analysis (RQA) and principal component analysis (PCA) to distinguish particular patterns for term and preterm birth. To date, these methods have not been used for the evaluation of bioelectrical activity in the uterus. To train novel classifiers for the EHG signals Support Vectors Machine classifications (multiclass SVM) was used. Statistical analysis was performed by means of non-parametric Mann-Whitney test.

Results: From among eleven parameters obtained from recurrence quantification analysis, five most appropriate were chosen: Recurrence Rate, Determinism, Laminarity, Entropy and Recurrence Period Density Entropy. Significant increase (p < .001) of Recurrence Rate was found in patients from group B, while increase of parameters, besides Laminarity, was found in patients from group A. The accuracy of classification obtained as a result of the analysis increased to 83,32%.

Conclusion: We showed that the respectively selected recurrence quantificators obtained for that time series could be used to classify all those signals to the appropriate group. The proposed analysis could help in detecting preterm labor based on the EHG signal dynamics.

Keywords: PCA analysis; Preterm labor; RQA analysis; SVM classification; Uterine EMG.