Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions

2019 ◽  
Vol 233 (3) ◽  
pp. 362-371
Author(s):  
N Punitha ◽  
S Ramakrishnan
Keyword(s):  
Gestational Age ◽  
Multifractal Analysis ◽  
Moving Average ◽  
Primary Source ◽  
Scaling Exponents ◽  
Average Technique ◽  
Singularity Exponents ◽  
Student’S T ◽  
Uterine Electromyography ◽  
Electromyography Signals

In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age > 37 weeks) and preterm (gestational age ≤ 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student’s t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant ( p < 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.

scholarly journals Eruption chronology of the first deciduous teeth in children born prematurely with birth weight less than 1500g

2014 ◽  
Vol 32 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Pedro Garcia F. Neto ◽  
Mario Cicero Falcao
Keyword(s):  
Birth Weight ◽  
Nutritional Status ◽  
Premature Infants ◽  
Gestational Age ◽  
Small For Gestational Age ◽  
Deciduous Teeth ◽  
Appropriate For Gestational Age ◽  
Eruption Age ◽  
Student’S T ◽  
Student’S T Test

Objective: To describe the eruption chronology of the first deciduous teeth in premature infants with birth weight less than 1500g and to compare it according to gender and nutritional status at birth. Methods: Longitudinal study including 40 low birth weight premature infants of both genders. The tooth was considered erupted when the crown went through the gum and became part of the oral environment. The comparison of the eruption chronology in relation to gender and among children appropriate or small for gestational age was done by Student's t-test, being significant p<0.05. Results: The eruption of the first tooth (teeth) occurred, on average, with 11.0±2.1 months of chronological age and with 9.6±1.9 months corrected for prematurity. The first erupted teeth were the lower central incisors. The average eruption for males was 9.7±1.9 and, for females, 9.5±1.9 months, both corrected for prematurity (p=0.98). The average eruption in children with birth weight appropriate for gestational age was 10.1±1.4 months; for small for gestational age, it was 9.4±2.2, also corrected for prematurity (p=0.07). Conclusions: The average eruption age of the first teeth, corrected for prematurity, was 9.6 months. Sex and nutritional status at birth did not change the eruption chronology.

Aminophylline effects on ventilatory response to hypoxia and hyperoxia in normal adults

1989 ◽  
Vol 67 (3) ◽  
pp. 1150-1156 ◽  
Author(s):  
D. Georgopoulos ◽  
S. G. Holtby ◽  
D. Berezanski ◽  
N. R. Anthonisen
Keyword(s):  
Moving Average ◽  
Minute Ventilation ◽  
Base Line ◽  
Breathing Patterns ◽  
Average Technique ◽  
Carotid Bodies ◽  
Before And After ◽  
Arterial O2 Saturation ◽  
Hyperoxic Ventilation ◽  
Breathing Room

In 10 normal young adults, ventilation was evaluated with and without pretreatment with aminophylline, an adenosine blocker, while they breathed pure O2 1) after breathing room air and 2) after 25 min of isocapnic hypoxia (arterial O2 saturation 80%). With and without aminophylline, 5 min of hyperoxia significantly increased inspiratory minute ventilation (VI) from the normoxic base line. In control experiments, with hypoxia, VI initially increased and then declined to levels that were slightly above the normoxic base line. Pretreatment with aminophylline significantly attenuated the hypoxic ventilatory decline. During transitions to pure O2 (cessation of carotid bodies' output), VI and breathing patterns were analyzed breath by breath with a moving-average technique, searching for nadirs before and after hyperoxia. On placebo days, at the end of hypoxia, hyperoxia produced nadirs that were significantly lower than those observed with room-air breathing and also significantly lower than when hyperoxia followed normoxia, averaging, respectively, 6.41 +/- 0.52, 8.07 +/- 0.32, and 8.04 +/- 0.39 (SE) l/min. This hypoxic depression was due to significant decrease in tidal volume and prolongation of expiratory time. Aminophylline partly prevented these alterations in breathing pattern; significant posthypoxic ventilatory depression was not observed. We conclude that aminophylline attenuated hypoxic central depression of ventilation, although it does not affect hyperoxic steady-state hyperventilation. Adenosine may play a modulatory role in hypoxic but not in hyperoxic ventilation.

DEWMA Control Chart for the Coefficient of Variation

2011 ◽  
Vol 201-203 ◽  
pp. 1682-1688 ◽  
Author(s):  
Eui Pyo Hong ◽  
Hae Woon Kang ◽  
Chang Wook Kang
Keyword(s):  
Control Chart ◽  
Coefficient Of Variation ◽  
Control Charts ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Average Technique ◽  
Small Shift ◽  
Production Run ◽  
Weighted Moving Average ◽  
Exponentially Weighted

When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. The CV-EWMA (exponentially weighted moving average) control chart which was developed recently is effective in detecting a small shifts of CV. In this paper, we propose the CV-DEWMA control chart, combining the DEWMA (double exponentially weighted moving average) technique. We show that CV-DEWMA control chart perform better than CV-EWMA control chart in detecting small shifts when sample size n is larger than 5.

scholarly journals Using Network Theory and Machine Learning to predict El Niño

2018 ◽  
Author(s):  
Peter D. Nooteboom ◽  
Qing Yi Feng ◽  
Cristóbal López ◽  
Emilio Hernández-García ◽  
Henk A. Dijkstra
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
El Niño ◽  
Lead Time ◽  
El Nino ◽  
Southern Oscillation ◽  
Moving Average ◽  
Ensemble Prediction ◽  
Type Model ◽  
Average Technique

Abstract. The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag of six months. In this paper, we aim to increase this prediction skill at lags up to one year. The new method to do so combines a classical Autoregressive Integrated Moving Average technique with a modern machine learning approach (through an Artificial Neural Network). The attributes in such a neural network are derived from topological properties of Climate Networks and are tested on both a Zebiak–Cane-type model and observations. For predictions up to six months ahead, the results of the hybrid model give a better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Moreover, results for a twelve-month lead time prediction have a similar skill as the shorter lead time predictions.

DETECTION OF ATMOSPHERIC TURBULENCE BY MULTIFRACTAL ANALYSIS USING WAVELETS

Fractals ◽  
2004 ◽  
Vol 12 (02) ◽  
pp. 211-221 ◽  
Author(s):  
M. NICOLLET ◽  
A. LEMARCHAND ◽  
N. CAVACIUTI
Keyword(s):  
Wavelet Transform ◽  
Atmospheric Turbulence ◽  
Multifractal Analysis ◽  
Function Method ◽  
Lower Stratosphere ◽  
Thin Layers ◽  
Scaling Exponents ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima ◽  
Balloon Measurements

We study the singularities of a temperature profile obtained by means of balloon measurements in the troposphere and lower stratosphere. The data give the evolution of the temperature as the altitude of the probe increases. We compare the scaling exponents deduced from the Wavelet Transform Modulus Maxima (WTMM) method and the structure function method. In the lower stratosphere, the variations in the multifractal properties with the altitude deduced from wavelets allow us to detect thin layers of about 200 m depth exhibiting atmospheric turbulence.

scholarly journals Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

2018 ◽  
Vol III (IV) ◽  
pp. 413-426
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Market Index ◽  
Short Term ◽  
Average Technique ◽  
Autoregressive Integrated Moving Average ◽  
Past Behavior ◽  
Market Index ◽  
Arima Modeling

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

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