Bassam Moslem

Classification performance of the frequency-related parameters derived from uterine EMG signals

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 to their classification performances (pregnancy vs. labor) using the receiver operating characteristic (ROC) curve analysis. The comparison between the parameters indicates that median frequency is the best frequency-related parameter that can be used for distinguishing between pregnancy and labor contractions. We conclude that median frequency can be the representative frequency-related parameter for classification problems of uterine EMG.

Classification of multichannel uterine EMG signals

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 fusion method based on these classification performances was tested. After fusion, the network yielded better classification accuracy than any individual channel could provide. The high percentage of correctly classified labor/non-labor events proves the efficiency of multichannel recordings in detecting labor. These findings can be very useful for the aim of classifying antepartum versus labor patients.

A multisensor data fusion approach for improving the classification accuracy of uterine EMG signals

Multisensor data fusion is an important technique used for solving various pattern recognition problems. In this paper, we used data fusion for improving the classification of uterine electromyogram (EMG) signals recorded by 16 electrodes positioned on the abdominal wall of the pregnant women. First, we evaluated the classification performance of each channel. Then, we applied a decision-level fusion method based first on the majority voting (MV), then on the weighted majority voting (WMV) rules. The results were very promising. The fusion of data from multiple sensors improved the accuracy of uterine EMG classification. The high percentage of correctly classified events, compared with earlier results, proves the efficiency of this approach for detecting labor.

Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals

Multisensor data fusion is a powerful solution for solving difficult pattern recognition problems such as the classification of bioelectrical signals. It is the process of combining information from different sensors to provide a more stable and more robust classification decisions. We combine here data fusion with multiresolution analysis based on the wavelet packet transform (WPT) in order to classify real uterine electromyogram (EMG) signals recorded by 16 electrodes. Herein, the data fusion is done at the decision level by using a weighted majority voting (WMV) rule. On the other hand, the WPT is used to achieve significant enhancement in the classification performance of each channel by improving the discrimination power of the selected feature. We show that the proposed approach tested on our recorded data can improve the recognition accuracy in labor prediction and has a competitive and promising performance.

The smartphone accessory heart rate monitor

This paper describes a design for a smartphone accessory that aims to determine the human heart rate, especially for cardiac patients who need to monitor their heart rate, it being an important indicator for prognosis and diagnosis, and also share it with their physician anytime to seek medical advice when needed. The goal of this paper is to present our design of this compact sized and user friendly smartphone accessory that can be accounted in clinical care and practice.

Classification of uterine EMG signals by using Normalized Wavelet Packet Energy

The aim of this work is to detect labor by classifying uterine electromyogram (EMG) signals into 2 classes: pregnancy and labor. Based on the fact that the energy distribution of the uterine EMG signals varies throughout pregnancy, the recorded signals are first decomposed into a 3-level wavelet packet tree then the Normalized Wavelet Packets Energies (NWPEs) are calculated and used to classify the signals. The high percentage of correctly classified events indicates that the use of NWPE can be a suitable choice for solving classification problems of electrophysiological signals and proves the efficiency of this approach in detecting labor.

Combining multiple support vector machines for boosting the classification accuracy of uterine EMG signals

A defining feature of physiological systems is the complexity both in their structures and functions. As a result, classifying physiological data is a difficult task. In this paper, we propose the use of a committee machines with a Support Vector Machines (SVM) as the component classifier in order to boost the classification accuracy of multichannel uterine electromyogram (EMG) signals. The approach was applied on each channel and a majority voting rule was used in order to determine the final decision of the committee. The results indicate that a committee machines exhibits performance unobtainable by an individual committee member on its own. We conclude that this approach can improve the recognition accuracy and has a competitive and promising performance.

Classification of multichannel uterine EMG signals by using a weighted majority voting decision fusion rule

Recording the bioelectrical signals by using multiple sensors has been the subject of considerable research effort in the recent years. The multisensor recordings have opened the way to the application of more advanced signal processing techniques and the extraction of new parameters. The focus of this paper is to demonstrate the importance of multisensor recordings for classifying multichannel uterine EMG signals recorded by 16 electrodes. First, we showed that mapping the characteristics of the multichannel uterine EMG signals may allow to set some peculiar properties of these channels. Then, data recorded from each channel were individually classified. Based on the variability between the classification performances of each channel, a weighted majority voting (WMV) decision fusion rule was applied. The classification network yielded better classification accuracy than any individual channel could provide. We conclude that our multichannel-based approach can be very useful to gain insight into the modification of the uterine activity and can improve the classification accuracy of pregnancy and labor contractions.

Detecting missing signals in multichannel recordings by using higher order statistics

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 on real uterine electromyogram (EMG) signals recorded by 4×4 electrode grid. Results have shown that HOS descriptors can discriminate between the two classes of signals (missing vs. non-missing). These results are supported by statistical analysis using the t-test which indicated good statistical significance of 95% confidence level.

Detrended Fluctuation Analysis of uterine electromyography

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 associated to an alteration in the long-range correlation of the uterine EMG recordings. The results obtained from the analyzed data indicate that the correlation in the contractions increases during pregnancy. Furthermore, we demonstrate that the long-range parameter may discriminate between the two classes (pregnancy/labor). The results are supported by statistical analysis using t-test indicating good statistical significance with a confidence level of 95%. A surrogate data test is also performed to investigate the nature of the underlying dynamics of our experimental data. The results are very promising for monitoring pregnancy and detecting labor and may help identify preterm labor.