Multifractal Analysis of Term and Preterm Uterine EMG Signals Using Wavelet Leaders
01 Oct 2018-
TL;DR: It appears that these multifractal features of uterine Electromyography signals can help in investigating the progressive changes in uterine muscle contractions during pregnancy and differentiates term and preterm conditions at an early stage.
Abstract: The purpose of this work is to analyze the multifractal features of uterine Electromyography (EMG) signals for the progression of pregnancy in term condition and to differentiate term (period $> 37$ weeks of gestation) and preterm (period $\leq 37$ weeks of gestation) conditions using Wavelet Leaders (WL) algorithm. For this study, the signals recorded from the surface of abdomen during the second (T1 and P1) and third trimester (T2) are considered from an online database. The signals are preprocessed and multifractal analysis is applied to compute the multifractal spectrum. Three features such as minimum $(\alpha_{\min})$ , maximum $(\alpha_{\max})$ and peak $(\alpha_{0})$ singularity exponents are extracted from the multifractal spectrum for analyzing the signals in T1, T2 and P1 groups. It is observed that there is a shift in the spectrum with increase in the order of wavelet. $\alpha_{\min}$ and $\alpha_{\max}$ are able to differentiate signals in T1-P1 and T1- T2 groups respectively. $\alpha_{0}$ is found to be consistent and has statistical significance in discriminating signals in all the considered groups. Hence, it appears that these multifractal features can help in investigating the progressive changes in uterine muscle contractions during pregnancy and differentiates term and preterm conditions at an early stage.
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TL;DR: In this article, the traces of possible volatility bubble emerge when it is positioned against its own lags (both lag 1 and lag 2) and the volatility trigger indicated clear traces of herding and an embedded parabola function.
Abstract: This study delves into the herding and bubble detection in the volatility domain of a capital market underlying. Furthermore, it focuses on creating heuristics, so that common investors find it relatively easy to understand the state of the market volatility. Hence, it can be termed that this study is focused on the specific financial innovation regarding bubble and herding detection coupled with investor awareness. The traces of possible volatility bubble emerge when it is positioned against its own lags (both lag1 and lag2). The volatility trigger indicated clear traces of herding and an embedded parabola function. Continuous and repetitive parabola function hinted at a subtle presence of “fractals”. Firstly, the detrended fluctuation analysis has been used with its multifractal variant. Secondly, the regularized form of Hurst calculation and analysis have been used. Both tests reveal the traces of nascent bubble formation owing to prominent herding in CNX Nifty HFT environment. They also indicate a clear link with Hausdorff topological patterns. These patterns would help to create heuristics, enabling investors to be aware of possible bubble and herd situations.
13 citations
Cites background from "Multifractal Analysis of Term and P..."
...The plot of hq versus Dq is referred to as the “multifractal spectrum” (Ihlen, 2012; Vardhini et al., 2018)....
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References
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TL;DR: Various linear and non-linear signal-processing techniques were applied to three-channel uterine EMG records to separate term and pre-term deliveries, showing noticeable differences between term delivery records recorded before and after the 26th week.
Abstract: Various linear and non-linear signal-processing techniques were applied to three-channel uterine EMG records to separate term and pre-term deliveries. The linear techniques were root mean square value, peak and median frequency of the signal power spectrum and autocorrelation zero crossing; while the selected non-linear techniques were estimation of the maximal Lyapunov exponent, correlation dimension and calculating sample entropy. In total, 300 records were grouped into four groups according to the time of recording (before or after the 26th week of gestation) and according to the total length of gestation (term delivery records--pregnancy duration >or=37 weeks and pre-term delivery records--pregnancy duration <37 weeks). The following preprocessing band-pass Butterworth filters were tested: 0.08-4, 0.3-4, and 0.3-3 Hz. With the 0.3-3 Hz filter, the median frequency indicated a statistical difference between those term and pre-term delivery records recorded before the 26th week (p = 0.03), and between all term and all pre-term delivery records (p = 0.012). With the same filter, the sample entropy indicated statistical differences between those term and pre-term delivery records recorded before the 26th week (p = 0.035), and between all term and all pre-term delivery records (p = 0.011). Both techniques also showed noticeable differences between term delivery records recorded before and after the 26th week (p
211 citations
"Multifractal Analysis of Term and P..." refers background or methods in this paper
...This can be due to the increase in the intensity of contraction of the uterine muscles with increase in the gestational age [13]....
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...3 - 3 Hz is used in this study, since it is free from the artifacts [13]....
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...The uterine EMG signals are obtained from the TermPreterm Electrohysterogram database, from Physionet, which consists of 300 records [13]....
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TL;DR: A supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records, is presented, showing an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.
Abstract: There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.
138 citations
"Multifractal Analysis of Term and P..." refers background in this paper
...Preterm labor refers to the birth of neonates before 37 weeks of gestation, while term condition refers to the period of pregnancy greater than 37 weeks [1]....
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TL;DR: In this paper, the authors investigated the multifractal structure of the interevent times between successive earthquakes that occurred in Umbria-Marche, which is one of the most seismically active areas of central Italy.
Abstract: We investigated the multifractal structure of the interevent times between successive earthquakes that occurred in Umbria-Marche, which is one of the most seismically active areas of central Italy. We used the Multifractal Detrended Fluctuation Analysis (MF-DFA), which permits detection of multifractality in nonstationary series. Analyzing the time evolution of the multifractal behaviour of the seismic sequence, a loss of multifractality during the aftershocks is revealed.
107 citations
"Multifractal Analysis of Term and P..." refers background in this paper
...The multifractal spectrum is characterized by analyzing features such as, maximum singularity exponent (αmax), minimum singularity exponent (αmin) and peak singularity exponent (α0) of the spectrum [14, 15]....
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TL;DR: It is demonstrated that the joint use of wavelet Leaders, log-cumulants, and bootstrap procedures enable a powerful tool for testing the multifractal properties of data that is practically effective and can be applied to a single observation of data with finite length.
Abstract: Multifractal analysis, which mostly consists of measuring scaling exponents, is becoming a standard technique available in most empirical data analysis toolboxes. Making use of the most recent theoretical results, it is based here on the estimation of the cumulants of the log of the wavelet leaders, an elaboration on the wavelet coefficients. These log-cumulants theoretically enable discrimination between mono- and multifractal processes, as well as between simple log-normal multifractal models and more advanced ones. The goal of the present contribution is to design nonparametric bootstrap hypothesis tests aiming at testing the nature of the multifractal properties of stochastic processes and empirical data. Bootstrap issues together with six declinations of test designs are analyzed. Their statistical performance (significances, powers, and p-values) are assessed and compared by means of Monte Carlo simulations performed on synthetic stochastic processes whose multifractal properties (and log-cumulants) are known theoretically a priori. We demonstrate that the joint use of wavelet Leaders, log-cumulants, and bootstrap procedures enable us to obtain a powerful tool for testing the multifractal properties of data. This tool is practically effective and can be applied to a single observation of data with finite length.
106 citations
"Multifractal Analysis of Term and P..." refers background or methods in this paper
...Wavelet Leaders has significant advantages over the wavelet coefficients for the multifractal formalism [11]....
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...For the computation of the time-localized suprema, the coefficients of the wavelets are required to be obtained from a compactly supported wavelet [11]....
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TL;DR: This work analyzes and compares Wavelet Leaders with the well known Multifractal Detrended Fluctuation Analysis, a comprehensible and well adapted method for natural and weakly stationary signals.
Abstract: Wavelet Leaders is a novel alternative based on wavelet analysis for estimating the Multifractal Spectrum It was proposed by Jaffard and co-workers improving the usual wavelet methods In this work, we analyze and compare it with the well known Multifractal Detrended Fluctuation Analysis The latter is a comprehensible and well adapted method for natural and weakly stationary signals Alternatively, Wavelet Leaders exploits the wavelet self-similarity structures combined with the Multiresolution Analysis scheme We give a brief introduction on the multifractal formalism and the particular implementation of the above methods and we compare their effectiveness We expose several cases: Cantor measures, Binomial Multiplicative Cascades and also natural series from a tonic–clonic epileptic seizure We analyze the results and extract the conclusions
99 citations
"Multifractal Analysis of Term and P..." refers background in this paper
...It is reported that wavelets are natural tools in multifractal analysis since it has implicit self-similarity property [10]....
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