Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals
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RESEARCH
Open Access
Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals Bassam Moslem1,2*, Mohamad Diab3, Mohamad Khalil2 and Catherine Marque1
Abstract 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. Keywords: Multichannel analysis, Data fusion, Wavelet packet Transform (WPT), Uterine electromyogram (EMG), Labor detection
Background Bioelectrical signals express the electrical functionality of different organs in the human body. The uterine electromyogram (EMG) signal, also called electrohysterogram (EHG), is one important signal among all bioelectrical signals. Recorded noninvasively from the abdominal wall of pregnant women, uterine EMG represents an objective and noninvasive way to quantify the uterine electrical activity. Studies have shown that uterine EMG can provide valuable information about the function aspects of the uterine contractility [1,2]. In addition, it is potentially the best predictor of preterm labor and of great value for the diagnosis of preterm delivery [3]. Although analyzing the uterine electrical activity represents an active research area, little attention has been brought to the classification of uterine EMG. In the literature, there exist only a few studies dealing with the classification of uterine EMG signals. In particular, Maner and Garfield * Correspondence: [email protected] 1 UMR CNRS 6600, laboratoire Biomécanique et Bio-ingénierie, Université de Technologie de Compiègne, Compiègne 60205, France 2 Azm Center for Research in Biotechnology and its applications, LASTRE Laboratory, Lebanese University, Tripoli, Lebanon Full list of author information is available at the end of the article
[4] used a kohonen method in order to classify uterine EMG data into term/preterm and labor/non-labor classes. Uterine contractions were quantified by finding the mean and the standard deviation of the power spectrum peak frequency, burst duration, number of bursts per unit time, and total burst activity. The approach applied on a total of 134 term and 51 preterm women yielded a classification accuracy of 80%. Moreover, Lu et al. [5] presented a classificatio
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