Preterm-term birth classification using EMD-based time-domain features of single-channel electrohysterogram data.

Publisher: Springer Country of Publication: Switzerland NLM ID: 101760671 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2662-4737 (Electronic) Linking ISSN: 26624729 NLM ISO Abbreviation: Phys Eng Sci Med Subsets: MEDLINE

Original Publication: Switzerland : Springer, [2020]-

Premature Birth* ; Term Birth*; Electromyography ; Female ; Humans ; Infant, Newborn ; Signal Processing, Computer-Assisted ; Uterus

Preterm birth anticipation is a crucial task that can reduce both the rate and the complications of preterm birth. Electrohysterogram (EHG) or uterine electromyogram (EMG) data have shown that they can provide useful information for preterm birth anticipation. Four distinct time-domain features (mean absolute value, average amplitude change, difference in absolute standard deviation value, and log detector) that are commonly applied to EMG signal processing were utilized and investigated in this study. A single channel of EHG data was decomposed into its constituent components (i.e., into intrinsic mode functions) by using empirical mode decomposition (EMD) before their time-domain features were extracted. The time-domain features of the intrinsic mode functions of the EHG data associated with preterm and term births were applied for preterm-term birth classification by using a support vector machine with a radial basis function. The preterm-term birth classifications were validated by using 10-fold cross validation. From the computational results, it was shown that excellent preterm-term birth classification can be achieved by using single-channel EHG data. The computational results further suggested that the best overall performance concerning preterm-term birth classification was obtained when thirteen (out of sixteen) EMD-based time-domain features were applied. The best accuracy, sensitivity, specificity, and [Formula: see text]-score achieved were 0.9382, 0.9130, 0.9634, and 0.9366, respectively.
(© 2021. Australasian College of Physical Scientists and Engineers in Medicine.)

WHO (1976) WHO: recommended definitions, terminology and format for statistical tables related to the perinatal period and use of a new certificate for cause of perinatal deaths. Acta Obstet Gynecol Scand 56:247–253.
Blencowe H, Cousens S, Chou D, Oestergaard M, Say L, Moller AB et al (2013) Born too soon: the global epidemiology of 15 million preterm births. Reprod Health 10(suppl 1):S2. (PMID: 10.1186/1742-4755-10-S1-S2)
Mayo Clinic. Premature birth; 2021 [cited May 27, 2021]. https://www.mayoclinic.org/diseases-conditions/premature-birth/symptoms-causes/syc-20376730.
Wolrd Health Organization. Preterm birth; 2018 [cited May 27, 2021]. https://www.who.int/news-room/fact-sheets/detail/preterm-birth.
Centers for Disease Control and Prevention. Preterm birth; 2020 [cited May 27, 2021]. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pretermbirth.htm.
Fele-Žorž G, Kavšek G, Novak-Antolič v, 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. Med Biol Eng Comput 46:911–922. (PMID: 10.1007/s11517-008-0350-y)
Maner WL, Garfield RE (2007) Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng 35:465–473. (PMID: 10.1007/s10439-006-9248-8)
Leman H, Marque C, Gondry J (1999) Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Trans Biomed Eng 46:1222–1229. (PMID: 10.1109/10.790499)
Fergus P, Cheung P, Hussain A, Al-Jumeily D, Dobbins C, Iram S (2013) Prediction of preterm deliveries from EHG signals using machine learning. PLoS ONE 8:e77154. (PMID: 10.1371/journal.pone.0077154)
Peng J, Hao D, Yang L, Du M, Song X, Jiang H et al (2020) Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest. Biocybern Biomed Eng 40:352–362. (PMID: 10.1016/j.bbe.2019.12.003)
Kaleem AM, Kokate RD (2021) Prediction of pre-term groups from EHG signals using optimal multi-kernel SVM. J Ambient Intell Humaniz Comput 12:3689–3703. (PMID: 10.1007/s12652-019-01648-w)
Hussain AJ, Fergus P, Al-Askar H, Al-Jumeily D, Jager F (2015) Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women. Neurocomputing 151:963–974. (PMID: 10.1016/j.neucom.2014.03.087)
You J, Kim Y, Seok W, Lee S, Sim D, Park KS et al (2019) Multivariate time-frequency analysis of electrohysterogram for classification of term and preterm labor. J Electr Eng Technol 14:897–916. (PMID: 10.1007/s42835-019-00118-9)
Hemthanon C, Janjarasjitt S (2019) Examination of time-domain features of EHG data for preterm-term birth classification. J Comput 30:41–54.
Fergus P, Idowu I, Hussain A, Dobbins C (2016) Advanced artificial neural network classification for detecting preterm births using EHG records. Neurocomputing 188:42–49. (PMID: 10.1016/j.neucom.2015.01.107)
Hemthanon C, Janjarasjitt S (2020) Correlation between time-domain features of electrohysterogram data of pregnant women and gestational age. In: Lin KP, Magjarevic R, de Carvalho P (eds) IFMBE Proceedings. vol 74, pp 212–218.
Acharya UR, Sudarshan VK, Rong SQ, Tan Z, Lim CM, Koh JE et al (2017) Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Comput Biol Med 85:33–42. (PMID: 10.1016/j.compbiomed.2017.04.013)
Ren P, Yao S, Li J, Valdes-Sosa PA, Kendrick KM (2015) Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals. PLoS ONE 10:e0132116. (PMID: 10.1371/journal.pone.0132116)
Chen L, Hao Y (2017) Feature extraction and classification of EHG between pregnancy and labour group using Hilbert-Huang transform and extreme learning machine. Comput Math Methods Med 10:10. https://doi.org/10.1155/2017/7949507. (PMID: 10.1155/2017/7949507)
Smrdel A, Jager F (2015) Separating sets of term and pre-term uterine EMG records. Physiol Meas 36:341–355. (PMID: 10.1088/0967-3334/36/2/341)
Mas-Cabo J, Ye-Lin Y, Garcia-Casado J, Díaz-Martinez A, Perales-Marin A, Monfort-Ortiz R et al (2020) Robust characterization of the uterine myoelectrical activity in different obstetric scenarios. Entropy 22:743. (PMID: 10.3390/e22070743)
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A. 454:903–995. (PMID: 10.1098/rspa.1998.0193)
Huang NE, Shen SS (2014) Hilbert-Huang transform and its applications, 2nd edn. World Scientific, New Jersey. (PMID: 10.1142/8804)
Fontugne R, Borgnat P, Flandrin P (2017) Online empirical mode decomposition. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4306–4310.
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220. (PMID: 10.1161/01.CIR.101.23.e215)
Huang NE, Wu MLC, Long SR, Shen SSP, Qu W, Gloersen P et al (2003) A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc R Soc A 459:2317–2345. (PMID: 10.1098/rspa.2003.1123)
Tibshirani Hastie R T, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New York.
Abe S (2010) Support vector machines for pattern classification, 2nd edn. Springer, London. (PMID: 10.1007/978-1-84996-098-4)
Gu S, Tan Y, He X (2010) Discriminant analysis via support vectors. Neurocomputing 73:1669–1675. (PMID: 10.1016/j.neucom.2009.09.021)

RSA6180041 Thailand Research Fund

Keywords: Electrohysterogram; Empirical mode decomposition; Preterm birth; Support vector machine; Time-domain features

Date Created: 20210831 Date Completed: 20211216 Latest Revision: 20211216

20240829

10.1007/s13246-021-01051-w

34463948