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In respect to the main goal of our ongoing work for estimating the heart rate variability (HRV) from fetal electrocardiogram (FECG) signals for monitoring the health of the fetus, we investigate in this paper the possibility of extracting the fetal heart rate variability (HRV) directly from the abdominal composite recordings. Our proposed approach is based on a combination of two techniques: Periodic...
In respect to the main goal of our ongoing work for analyzing fetal electrocardiogram (FECG) signals for monitoring the health of the fetus, we investigate in this paper the possibility of extracting the fetal heart rate (FHR) directly from the abdominal composite recordings. Our proposed approach is based on a combination of Independent Component Analysis (ICA) and least mean square (LMS) adaptive...
The prevention of preterm labor remains one of the primary goals of obstetric research. One way to achieve this goal effectively is to understand the mechanisms regulating the uterine contractility. Herein, we evaluate the correlation between uterine electrical activities recorded from spatially-distributed regions by calculating the nonlinear regression coefficient. Results have shown that, during...
In real world applications, a multichannel acquisition system is susceptible of having one or many of its sensors displaced or detached, leading therefore to the loss or corruption of the recorded signals. In this paper, we present a technique for detecting missing or corrupted signals in multichannel recordings. Our approach is based on Higher Order Statistics (HOS) analysis. Our approach is tested...
Classification of multichannel uterine electromyogram (EMG) signals is addressed. Signals were recorded by a matrix of 16 electrodes. First, signals corresponding to each channel were individually classified using an artificial neural network (ANN) based on radial basis functions (RBF). The results have shown that the classification performance varies from one channel to another. Then, a decision...
Frequency-related parameters derived from the uterine electromyogram (EMG) signals are widely used in many pregnancy monitoring and preterm delivery prediction studies. Although they are classical parameters, they are well suited for quantifying uterine EMG signals and have many advantages over amplitude-related parameters. The present work aims to compare various frequency-related parameters according...
In respect to the main goal of our ongoing work for predicting preterm birth, we analyze in this paper uterine EMG recordings of 11 pregnant and laboring women by means of Detrended Fluctuation Analysis (DFA), a scaling analysis method that quantifies a simple parameter to represent the correlation properties of a time series. Our study provides convincing evidence that pregnancy progress is typically...
In respect to the main goal of our ongoing work for predicting preterm birth, we analyze in this paper the complexity of the uterine electromyography (EMG) by using the sample entropy (SampEn) algorithm. By considering recent methodological developments, we measure the SampEn over multiple scales using the wavelet packet decomposition method. The results obtained from the analyzed data indicate that...
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