Home > Archives > NCAEAS2312 IJSRST-Library

EHG Signal Classification for Term and Pre-Term Pregnancy Analysis

Authors(2) :-Uzma Fatima, Prof. Tirupati Goskula

Early prediction of premature pregnancy reduces neonatal death and helps in adoption of treatment well suited for the pre-term pregnancy state. There are scads of work done in the area of term and pre-term pregnancy analysis like artificial intelligence, regressive models, and higher order statistical models. This paper proposes a four-level decomposition of Electrohysterography (EHG) signals using Discrete Wavelet Transform (DWT) based on pyramid algorithm to obtain the final feature vector matrix. Classification is done using Support Vector Machines (SVM) by dividing the data into test and training sets. It is validated on a well-known benchmark database from Physionet Database. The proposed method can be used for real time implementation owing to low computational cost, high speed and its feasibility to be implemented on hardware. The encouraging experimental results show that the technique gives an accuracy of 97.8% and can be a promising tool for investigating the risk of preterm labor.
Uzma Fatima, Prof. Tirupati Goskula
Discrete wavelet transform, labor time detection, term and pre-term pregnancy, Support Vector Machines, EHG
    1. WHO (2012) Born too soon: The Global Action Report on Preterm Birth.
    1. Baker PN, Kenny L (2011) Obstetrics by Ten Teachers. Hodder Arnold Press. 436 p.
    2. Greenough A (2012) Long Term Respiratory Outcomes of very Premature Birth (,32 weeks). Semin Fetal Neonatal Med 17(2): 73–76.
    3. Mangham LJ, Petrou S, Doyle LW, Draper ES, Marlow N (2009) The Cost of Preterm Birth Throughout Childhood in England and Wales. Pediatrics 123(2): 312–327.
    4. Rattihalli R, Smith L, Field D (2012) Prevention of preterm births: are we looking in the wrong place? Archives of disease in childhood. Fetal and neonatal 97(3): 160–1.
    5. Goldenberg RL, Culhane JF, Iams JD, Romero R (2008) Epidemiology and causes of preterm birth. The Lancet 371(9606): 75–84.
    6. McPheeters M, Miller WC, Hartmann KE, Savitz DA, Kaufman JS, et al. (2005) The Epidemiology of Threatened Premature Labor: A Prospective Cohort Study. American journal of obstetrics and gynaecology 192(4): 1325–9.
    7. Lucovnik M, Kuon RJ, Chambliss LR, Maner WL, Shi SQ, et al. (2011) Use of uterine electromyography to diagnose term and preterm labor. Acta Obstetricia et Gynecologica Scandinavica 90(2): 150–157.
    8. Muglia LJ, Katz M (2010) The Enigma of Spontaneous Preterm Birth. N Engl J Med 362(6): 529–35.
    9. Fele-Z ˇ orz ˇ G, Kavs ˇek G, Novak-Antolic ˇ Z, Jager F (2008) A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Medical & biological engineering & computing 46(9): 911–22.
    10. Doret M (2005) Uterine Electromyograpy Characteristics for early Diagnosis of Mifepristone-induced Preterm Labour. Obstetrics and Gynecology 105(4): 822–30.
    11. Moslem B, Khalil M, Diab MO, Chkeir A, Marque C (2011) A Multisensor Data Fusion Approach for Improving the Classification Accuracy of Uterine EMG Signals. 18th
    12. IEEE International Conference on Electronics, Circuits and Systems (ICECS): 93–96.
    13. Moslem B, Khalil M, Diab MO, Marque C (2012) Classification of multichannel uterine EMG signals by using a weighted majority voting decision fusion rule. 16th IEEE Mediterranean Electrotechnical Conference: 331–334.
    14. Moslem B, Khalil M, Diab M (2011) Combining multiple support vector machines for boosting the classification accuracy of uterine EMG signals. 18th IEEE International Conference on Electronics, Circuits and Systems (ICECS): 631–634.
    15. Moslem B, Karlsson B, Diab MO, Khalil M, Marque C (2011) Classification Performance of the Frequency-Related Parameters Derived from Uterine EMG Signals. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 3371–4.
    17. Moslem B, Diab MO, Khalil M, Marque C (2011) Classification of multichannel uterine EMG signals by using unsupervised competitive learning. IEEE Workshop on Signal Processing Systems: 267–272.
    18. Moslem B, Diab MO, Marque C, Khalil M (2011) Classification of multichannel Uterine EMG Signals. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 2602–5.
    19. Rabotti C, Mischi M, Oei SG, Bergmans JWM (2010) Noninvasive estimation of the electrohysterographic action-potential conduction velocity. IEEE transac-tions on bio-medical engineering 57(9): 2178–87.
    20. Buhimschi C, Boyle MB, Garfield RE (1997) Electrical activity of the human uterus during pregnancy as recorded from the abdominal surface. Obstetrics & Gynecology 90(1): 102–111.
    21. Lammers WJ (2013) The Electrical Activities of the Uterus During Pregnancy. Reproductive Sciences 20(2): 182–9.
    22. Garfield RE, Maner WL (2007) Physiology and Electrical Activity of Uterine Contractions. Seminars in Cell and Developmental Biology 18(3): 289–95.
    23. Gondry J, Marque C, Duchene J, Cabrol D (1993) Electrohysterography during Pregnancy: Preliminary Report. Biomedical Instrumentation and Technology/Association for the Advancement of Medical Instrumentation 27(4): 318–324.
    24. Lucovnik M, Maner WL, Chambliss LR, Blumrick R, Balducci J, et al. (2011) Noninvasive uterine electromyography for prediction of preterm delivery. American journal of obstetrics and gynecology 204(3): 228.e1–10.
    25. Leman H, Marque C, Gondry J (1999) Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE transactions on bio-medical engineering 46(10): 1222–9.
Publication Details
  Published in : Volume 3 | Issue 2 | January-February 2017
  Date of Publication : 2017-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 44-52
Manuscript Number : NCAEAS2312
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Uzma Fatima, Prof. Tirupati Goskula, "EHG Signal Classification for Term and Pre-Term Pregnancy Analysis", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 2, pp.44-52, January-February-2017.
Journal URL : http://ijsrst.com/NCAEAS2312

Article Preview