Analysis of uterine EMG signals in term and preterm conditions using generalised Hurst exponent features

Abstract

An attempt has been made in this Letter to analyse term (week of gestation (WOG) >37) and preterm (WOG ≤ 37) conditions using uterine electromyography (uEMG) signals and generalised Hurst exponent (GHE) features. For this analysis, public database signals recorded from the surface of abdomen are considered. Multifractal detrended fluctuation analysis is performed on the signals and the GHE is calculated. From the exponent, seven features are extracted and data-balancing based on synthetic minority over-sampling technique is used to retain a balanced feature contribution by the term and preterm records. Two classification algorithms namely, Naive Bayes and logistic regression (LR) are employed to classify the signals. Tenfold cross validation approach is executed and the performance is validated using accuracy, precision and recall. The results show the uEMG signals exhibit multifractal characteristics and five GHE features are significant in distinguishing the term and preterm uEMG signals. The LR classifier gives the highest accuracy of 97.8%. Therefore, it appears that the multifractal Hurst exponent features in combination with LR classifier can be used as biomarkers for predicting the preterm or term delivery during the early stage of gestation.

On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines

“…In terms of uterine contractions, there has been a surge of research work using machine learning models towards the development of pattern recognition algorithms that can proactively aid in the diagnosis of preterm predictions [ 3 , 11 , 12 , 13 , 14 , 15 , 16 ]. These works use contraction signals from patients who are in varied points of the third trimester of pregnancy, when distinct contraction signals begin to occur [ 3 , 11 , 12 , 13 , 14 , 15 , 16 ]. The results from these model‐based prediction exercises showed vastly impressive results for an array of signal processing, particularly non‐linear signal processing and machine learning models.…”

“…In terms of uterine contractions, there has been a surge of research work using machine learning models towards the development of pattern recognition algorithms that can proactively aid in the diagnosis of preterm predictions [3,[11][12][13][14][15][16]. These works use contraction signals from patients who are in varied points of the third trimester of pregnancy, when distinct contraction signals begin to occur [3,[11][12][13][14][15][16].…”

Application of Fractal Analysis based Feature Extractor for Channel Reduction of Silent Speech Interface Using Facial Electromyography

“…Multi fractal DFA was used by Garc´ıa-Espinosa et al [13] for the analysis of EMG signals in order to detect and treat tempero mandibular disorder in people. In the research domain of uterine electromyography (uEMG), DFA was used to estimate generalised hurst exponents (GHE) which can be utilised to classify signals [14]. In the same domain, DFA was also utilised for the forecast of preterm birth [15].…”