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.

[1]  M. Khalil,et al.  Complexity analysis of the uterine electromyography , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[2]  Catherine Marque,et al.  Uterine EHG Processing for Obstetrical Monitorng , 1986, IEEE Transactions on Biomedical Engineering.

[3]  Steve Thornton,et al.  Uterine electromyography signal feature extraction and classification , 2009, Int. J. Model. Identif. Control..

[4]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[5]  S. Wray,et al.  The Physiological Basis of Uterine Contractility: A Short Review , 2001, Experimental physiology.

[6]  Ronald R. Coifman,et al.  Local discriminant bases and their applications , 1995, Journal of Mathematical Imaging and Vision.

[7]  Catherine Marque,et al.  MODELING THE EFFECTS OF THE ELECTRODES POSITION ON THE SURFACE EMG CHARACTERISTICS , 2006 .

[8]  Elisabeth André,et al.  Fusion of Multichannel Biosignals Towards Automatic Emotion Recognition , 2009 .

[9]  Catherine Marque,et al.  Classification of multichannel uterine EMG signals by using unsupervised competitive learning , 2011, 2011 IEEE Workshop on Signal Processing Systems (SiPS).

[10]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Wei-Yang Lin,et al.  Recognizing Human Actions Using NWFE-Based Histogram Vectors , 2010, EURASIP J. Adv. Signal Process..

[12]  Holger Maul,et al.  The physiology of uterine contractions. , 2003, Clinics of Perinatology.

[13]  Gert Pfurtscheller,et al.  Automatic differentiation of multichannel EEG signals , 2001, IEEE Transactions on Biomedical Engineering.

[14]  Jon Atli Benediktsson,et al.  Consensus theoretic classification methods , 1992, IEEE Trans. Syst. Man Cybern..

[15]  J. Terrien,et al.  Improving the classification rate of labor vs. normal pregnancy contractions by using EHG multichannel recordings , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[16]  I. Verdenik,et al.  Uterine electrical activity as predictor of preterm birth in women with preterm contractions. , 2001, European journal of obstetrics, gynecology, and reproductive biology.

[17]  Sandy Rihana,et al.  Preterm labour detection by use of a biophysical marker: the uterine electrical activity , 2007, BMC pregnancy and childbirth.

[18]  M. Lucovnik,et al.  Noninvasive uterine electromyography for prediction of preterm delivery. , 2011, American journal of obstetrics and gynecology.

[19]  Holger Maul,et al.  Monitoring the progress of pregnancy and labor using electromyography. , 2009, European journal of obstetrics, gynecology, and reproductive biology.

[20]  C. Aring,et al.  A CRITICAL REVIEW , 1939, Journal of neurology and psychiatry.

[21]  Bruce Cumings,et al.  A short review , 1983 .

[22]  J. Terrien,et al.  Spatial analysis of uterine EMG signals: Evidence of increased in synchronization with term , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[24]  Karthikeyan Umapathy,et al.  Feature analysis of pathological speech signals using local discriminant bases technique , 2006, Medical and Biological Engineering and Computing.

[25]  Mohamad Khalil,et al.  Monitoring the Progress of Pregnancy and Detecting Labor Using Uterine Electromyography , 2009 .

[26]  William L. Maner,et al.  Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data , 2007, Annals of Biomedical Engineering.

[27]  Jan W. M. Bergmans,et al.  Noninvasive Estimation of the Electrohysterographic Action-Potential Conduction Velocity , 2010, IEEE Transactions on Biomedical Engineering.

[28]  Massimo Mischi,et al.  Inter-electrode delay estimators for electrohysterographic propagation analysis , 2009, Physiological measurement.

[29]  A. Zoubir,et al.  EURASIP Journal on Advances in Signal Processing , 2011 .

[30]  Mahmoud Hassan,et al.  Interactions between Uterine EMG at Different Sites Investigated Using Wavelet Analysis: Comparison of Pregnancy and Labor Contractions , 2010, EURASIP J. Adv. Signal Process..

[31]  C. Marque,et al.  Abdominal EHG on a 4 by 4 grid: mapping and presenting the propagation of uterine contractions , 2007 .

[32]  Aly Chkeir,et al.  A multisensor data fusion approach for improving the classification accuracy of uterine EMG signals , 2011, 2011 18th IEEE International Conference on Electronics, Circuits, and Systems.

[33]  Mandyam D. Srinath,et al.  Multichannel fusion models for the parametric classification of differential brain activity , 2005, IEEE Transactions on Biomedical Engineering.

[34]  Leslie Roberts,et al.  A Proof of Principle , 2007, Science.

[35]  Jon Atli Benediktsson,et al.  Classification of multisource and hyperspectral data based on decision fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[36]  David G. Stork,et al.  Pattern Classification , 1973 .

[37]  G. Saade,et al.  Instrumentation for the diagnosis of term and preterm labour , 1998, Journal of perinatal medicine.

[38]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[39]  Catherine Marque,et al.  Classification of multichannel uterine EMG signals , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  S M McGill,et al.  The importance of normalization in the interpretation of surface electromyography: a proof of principle. , 1999, Journal of manipulative and physiological therapeutics.

[41]  C Marque,et al.  Human abdominal EHG processing for uterine contraction monitoring. , 1989, Biotechnology.

[42]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[43]  C Marque,et al.  Uterine electromyography: a critical review. , 1993, American journal of obstetrics and gynecology.

[44]  Pramod K. Varshney,et al.  Multisensor Data Fusion , 1997, IEA/AIE.

[45]  C. Marque,et al.  Energy Distribution Analysis of Uterine Electromyography Signals , 2010 .

[46]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[47]  Massimo Mischi,et al.  Estimation of internal uterine pressure by joint amplitude and frequency analysis of electrohysterographic signals , 2008, Physiological measurement.

[48]  Sukhan Lee,et al.  Multisensor Fusion and Integration for Intelligent Systems , 2009 .

[49]  Jacques Duchêne,et al.  Uterine EMG analysis: a dynamic approach for change detection and classification , 2000, IEEE Transactions on Biomedical Engineering.

[50]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .

[51]  M. Mischi,et al.  Electrohysterographic conduction velocity estimation , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.