Uterine EMG Signals Spectral Analysis for Pre-Term Birth Prediction
A methodology for prediction of pre-term births is presented in this paper. The methodology is based on the analysis of EHG signals and data mining techniques. Initially, spectral and non-linear characteristics of the EHG are extracted, forming a pattern that is used to train a classifier to discriminate between term and pre-term cases. The method has been tested using a benchmark EHG database, and the obtained results indicate its effectiveness in accurate pre-term/term labour prediction.
Keywords:preterm delivery, electrohysterogram, EHG signal processing, uterine electromyogram
March of Dimes, PMNCH, Save the Children, WHO. Born Too Soon: The Global Action Report on Preterm Birth. Eds CP Howson, MV Kinney, JE Lawn. World Health Organization. Geneva, 2012
L. Liu, S. Oza, D. Hogan, Y. Chu, J. Perin, J. Zhu, J.E. Lawn, S. Cousens, C. Mathers, R.E. Black, “Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the Sustainable Development Goals”, Lancet, Vol. 388, pp. 3027-35, 2016 DOI: https://doi.org/10.1016/S0140-6736(16)31593-8
L. J. Mangham, S. Petrou, L. W. Doyle, E. S. Draper, N. Marlow, “The cost of preterm birth throughout childhood in England and Wales”, Pediatrics, Vol. 123, No. 2, pp. e312-e327, 2009 DOI: https://doi.org/10.1542/peds.2008-1827
S. Petrou, Z. Mehta, C. Hockley, P. Cook-Mozaffari, J. Henderson, M. Goldacre, “The impact of preterm birth on hospital inpatient admissions and costs during the first 5 years of life”, Pediatrics, Vol. 112, No. 6, pp. 1290-7, 2003 DOI: https://doi.org/10.1542/peds.112.6.1290
S. Petrou, “The economic consequences of preterm birth duringthe first 10 years of life”, BJOG: an International Journal of Obstetrics and Gynaecology, Vol. 112, No. S1, pp. 10-15, 2005 DOI: https://doi.org/10.1111/j.1471-0528.2005.00577.x
J. D. Iams, “Prediction and early detection of preterm labor”, The American College of Obstetricians and Gynecologists, Vol. 101, No. 2, pp. 402–412, 2003 DOI: https://doi.org/10.1016/S0029-7844(02)02505-X
C. Buhimschi, M. Boyle, R. Garfield, “Electrical activity of the human uterus during pregnancy as recorded from the abdominal surface”, Obstetrics & Gynecology, Vol. 90, pp. 102–111, 1997 DOI: https://doi.org/10.1016/S0029-7844(97)83837-9
G. Fele-Zorz, G. Kavsek, Z. Novak-Antolic, F. Jager, “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, Vol. 46, pp. 911-922, 2008 DOI: https://doi.org/10.1007/s11517-008-0350-y
P. Fergus, P. Cheung, A. Hussain, D. Al-Jumeily, C. Dobbins, S. Iram, “Prediction of preterm deliveries from ehg signals using machine learning”, PLOS ONE, Vol. 8, No. 10, Art. No. e77154, 2013 DOI: https://doi.org/10.1371/journal.pone.0077154
I. O. Idowu, P. Fergus, A. Hussain, C. Dobbins, H. Al-Askar, “Advance artificial neural network classification techniques using EHG for detecting preterm births”, 8th International Conference on Complex, Intelligent and Software Intensive Systems, Birmingham City University, Birmingham, UK, July 2-4, 2014 DOI: https://doi.org/10.1109/CISIS.2014.14
A. J. Hussain, P. Fergus, H. Al-Askar, D. Al-Jumeily, F. Jager, “Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women”, Neurocomputing, Vol. 151, pp. 963–974, 2015 DOI: https://doi.org/10.1016/j.neucom.2014.03.087
A. Smrdel, F. Jager, “Separating sets of term and pre-term uterine EMG records”, Physiological Measurement, Vol. 36, pp. 341–355, 2015 DOI: https://doi.org/10.1088/0967-3334/36/2/341
P. Fergus, I. Idowu, A. Hussain, C. Dobbins, “Advanced artificial neural network classification for detecting preterm births using EHG records”, Neurocomputing, Vol. 188, pp. 42–49, 2016 DOI: https://doi.org/10.1016/j.neucom.2015.01.107
U. R. Acharya, V. K. Sudarshan, S. Q. Rong, Z. Tan, C. M. Lim, J. E. W. Koh, S. Nayak, S. V. Bhandary, “Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals”, Computers in Biology and Medicine, Vol. 85, pp. 33-42, 2017 DOI: https://doi.org/10.1016/j.compbiomed.2017.04.013
S. M. Pincus, A. L. Goldberger, “Physiological time-series analysis: what does regularity quantify?”, American Journal of Physiology, Vol 266, pp. H1643-H1656, 1994 DOI: https://doi.org/10.1152/ajpheart.1994.266.4.H1643
L. Breiman, “Random forests”, Machine Learning, Vol. 45, No. 1, pp. 5–32, 2001 DOI: https://doi.org/10.1023/A:1010933404324
I. Brown, C. Mues, “An experimental comparison of classification algorithms for imbalanced credit scoring data sets”, Expert Systems with Applications, Vol. 39, pp. 3446–3453, 2012 DOI: https://doi.org/10.1016/j.eswa.2011.09.033
How to Cite
MetricsAbstract Views: 494
PDF Downloads: 315
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.