Differentiation of fluctuations in uterine contractions associated with Term pregnancies using adaptive fractal features of electromyography signals. (February 2021)

• Record Type: Journal Article
• Authors: Vardhini, P.; Punitha, N.; Ramakrishnan, S.
• Is Part Of: Medical engineering & physics. Volume 88(2021)
• Page Start: 78
• Page End: 85
• Publication Date: 2021-02
• Subjects: Uterine electromyography -- Adaptive fractal analysis -- Term condition -- Fluctuation function -- Hurst exponent; AFA adaptive fractal analysis -- CV coefficient of variation -- DFA detrended fluctuation analysis -- IUPC intrauterine pressure catheter -- Toco tocodynamometry -- uEMG uterine electromyography; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2020.12.010
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Analysis of uterine contractions are clinically significant for pregnancy monitoring. Non-invasive electromyography signals are found to be suitable for evaluation of uterine contractions. A method to differentiate fluctuations associated with uterine contractions using fractal methods and electromyography is proposed here. Fluctuation features are found be useful in differentiating Term pregnancies. Abstract: Analysis of uterine contractions using electromyography signals is gaining importance due to its capability to measure the dynamics of uterus. Uterine electromyography (uEMG) provides information on the nature of uterine contractions non-invasively. In this study, the fluctuations in uEMG signals associated with Term pregnancies are analyzed. For this, Term uEMG signals corresponding to second (T1) and third (T2) trimesters are considered. The signals are subjected to Adaptive Fractal Analysis (AFA), wherein a global trend is obtained by using overlapping windows of three orders namely, 25%, 50% and 75%. The signals are detrended and the fluctuation function is estimated. Two Hurst exponent features computed at short range ( Hs ) and long range ( Hl ) are extracted and statistically analyzed. Results show that AFA is able to characterize variations in the fluctuations of Term delivery signals. The feature values are observed to vary significantly during different weeks of gestation. It is found that features of T2 signals are higher than that of T1 signals for all the considered overlaps, indicating that T2 signals possess smoother characteristics than T1 signals. Further, coefficient of variation is observed to be low, indicating that these features are able to handle the inter-subject variations in Term signals. Therefore, it appears that the proposed approach could aid in investigation of progressive changes in uterine contractions during Term pregnancies.