Comparison of wavelet-based decomposition and empirical mode decomposition of electrohysterogram signals for preterm birth classification

다국어 초록 (Multilingual Abstract)

Signal decomposition is a computational technique that dissects a signal into its constituent components, providing supplementary information. In this study, the capability of two common signal decomposition techniques, including wavelet-based and empirical mode decomposition, on preterm birth classification was investigated. Ten time-domain features were extracted from the constituent components of electrohysterogram (EHG) signals, including EHG subbands and EHG intrinsic mode functions, and employed for preterm birth classification. Preterm birth classification and anticipation are crucial tasks that can help reduce preterm birth complications. The computational results show that the preterm birth classification obtained using wavelet-based decomposition is superior. This, therefore, implies that EHG subbands decomposed through wavelet-based decomposition provide more applicable information for preterm birth classification. Furthermore, an accuracy of 0.9776 and a specificity of 0.9978, the best performance on preterm birth classificationamong state-of-the-art signal processing techniques, were obtained using the time-domain features of EHG subbands.

참고문헌 (Reference)

1 M. Vetterli, "Wavelets and filter banks: theory and design" 40 : 2207-2232, 1992

2 WHO, "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" 56 : 247-253, 1976

3 K. Thijssen, "Uterine monitoring techniques from patients’ and users’ perspectives" 8 : e184-e191, 2018

4 I. Verdenik, "Uterine electrical activity as predictor of preterm birth in women with preterm contractions" 95 : 149-153, 2001

5 C. Marque, "Uterine EHG processing for obstetrical monitoring," 333 : 1182-1187, 1986

6 H. Leman, "Use of the electrohysterogram signal for characterization of contractions during pregnancy" 46 : 1222-1229, 1999

7 N. E. Huang, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis" 454 : 903-995, 1998

8 F. Jager, "Term-Preterm EHG Database"

9 I. Daubechies, "Ten lectures on wavelets" Society for industrial and applied mathematics 1992

10 M. A. Oskoei, "Support vector machine-based classification scheme for myoelectric control applied to upper limb" 55 : 1956-1965, 2008

11 H. de Lau, "Study protocol: PoPE-prediction of preterm delivery by electrohysterography" 14 : 192-, 2014

12 D. Tkach, "Study of stability of time-domain features for electromyographic pattern recognition" 7 : 21-, 2010

13 A. Smrdel, "Separating sets of term and pre-term uterine EMG records" 36 : 341-355, 2015

14 J. Mas-Cabo, "Robust characterization of the uterine myoelectrical activity in different obstetric scenarios" 22 : 743-, 2020

15 C. Rabotti, "Propagation of electrical activity in uterine muscle during pregnancy: a review" 213 : 406-416, 2015

16 Suparerk Janjarasjitt, "Preterm-term birth classification using EMD-based time-domain features of single-channel electrohysterogram data" Springer Science and Business Media LLC 44 (44): 1151-1159, 2021

17 C. K. Marque, "Preterm labour detection by use of a biophysical marker: the uterine electrical activity" 7 : S5-, 2007

18 Centers for Disease Control and Prevention, "Preterm birth"

19 World Health Organization, "Preterm birth"

20 Mayo Clinic, "Premature birth"

21 P. Fergus, "Prediction of preterm deliveries from EHG signals using machine learning" 8 : e77154-, 2013

22 A. M. Kaleem, "Prediction of pre-term groups from EHG signals using optimal multi-kernel SVM" 12 : 3689-3703, 2021

23 W. L. Maner, "Predicting term and preterm delivery with transabdominal uterine electromyography" 101 : 1254-1260, 2003

24 A. L. Goldberger, "Physiobank, physiotoolkit, and physio net: components of a new research resource for complex physiologic signals" 101 (101): e215-e220, 2000

25 A. Diab, "Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals" 36 : 761-767, 2014

26 I. Daubechies, "Orthonormal bases of compactly supported wavelets" 41 : 909-996, 1988

27 R. Fontugne, "Online empirical mode decomposition" 4306-4310, 2017

28 M. Lucovnik, "Noninvasive uterine electromyography for prediction of preterm delivery" 204 : 228.e1-228.e10, 2011

29 T. Y. Euliano, "Monitoring uterine activity during labor: a comparison of 3 methods" 208 : 66.e1-66.e6, 2013

30 P. Ren, "Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals" 10 : e0132116-, 2015

31 W. L. Maner, "Identification of human term and preterm labor using artificial neural networks on uterine electromyography data" 35 : 465-473, 2007

32 N. E. Huang, "Hilbert–Huang transform and its applications" World Scientific 2014

33 C. Hemthanon, "Future trends in biomedical and health informatics and cybersecurity in medical devices vol. 74" Springer 2020

34 P. Pudil, "Floating search methods in feature selection" 15 : 1119-1125, 1994

35 Lili Chen, "Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine" Hindawi Limited 2017 : 1-9, 2017

36 C. Hemthanon, "Examination of time-domain features of EHG data for preterm-term birth classification," 30 : 41-54, 2019

37 S. Janjarasjitt, "Examination of single wavelet-based features of EHG signals for preterm birth classification" 44 : 212-218, 2017

38 S. Janjarasjitt, "Evaluation of performance on preterm birth classification using single wavelet-based features of EHG signals" 1-4, 2017

39 J. Peng, "Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest" 40 : 352-362, 2020

40 M. W. C. Vlemminx, "Electrohysterography for uterine monitoring during term labour compared to external tocodynamometry and intra-uterine pressure catheter" 215 : 197-205, 2017

41 C. Buhimschi, "Electrical activity of human uterus during pregnancy as recorded from the abdominal surface" 90 : 102-111, 1997

42 M. Zardoshti-Kermani, "EMG feature evaluation for movement control of upper extremity prostheses" 3 : 324-333, 1995

43 A. J. Hussain, "Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women" 151 : 963-974, 2015

44 A. Grossman, "Decomposition of hardy functions into square integrable wavelets of constant shape" 15 : 723-736, 1984

45 Kang Soo Kim ; 최흥호 ; Chang Soo Moon ; 문치웅, "Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions" 한국물리학회 11 (11): 740-745, 2011

46 D. Alamedine, "Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor" Hindawi Limited 2013 : 1-9, 2013

47 M. Hassan, "Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals" 33 : 980-986, 2011

48 F. J. Ferri, "Comparative study of techniques for large-scale feature selection" 16 : 403-413, 1994

49 B. Moslem, "Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals" 2012 : 167-, 2012

50 F. Jager, "Characterization and automatic classification of preterm and term uterine records" 13 : e0202125-, 2018

51 H. Blencowe, S. Cousens, D. Chou, M. Oestergaard, L. Say, A. -B. Moller, M. Kinney, and J. Lawn, "Born too soon: the global epidemiology of 15 million preterm births" 10 : S2-, 2013

52 U. R. Acharya, "Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals" 85 : 33-42, 2017

53 I. Guyon, "An introduction to variable and feature selection" 3 : 1157-1182, 2003

54 T. Fawcett, "An introduction to ROC analysis" 27 : 861-874, 2006

55 P. Fergus, "Advanced artificial neural network classification for detecting preterm births using EHG records" 188 : 42-49, 2016

56 H. He, "ADASYN: adaptive synthetic sampling approach for imbalanced learning" 1322-1328, 2008

57 S. Mallat, "A wavelet tour of signal processing" Academic Press 1998

58 S. G. Mallat, "A theory for multiresolution signal decomposition:the wavelet representation" 11 : 674-693, 1989

59 N. E. Huang, "A confidence limit for the empirical mode decomposition and Hilbert spectral analysis" 459 : 2317-2345, 2003

60 G. FeleŽorž, "A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and preterm delivery groups" 46 : 911-922, 2008