과제정보
This work is supported by a TRF Research Career Development Grant, jointly funded by the Thailand Research Fund (TRF) and the Ubon Ratchathani University, under the Contract No. RSA6180041.
참고문헌
- A. Grossman and J. Morlet, Decomposition of hardy functions into square integrable wavelets of constant shape, SIAM J. Math. Anal. 15 (1984), 723-736. https://doi.org/10.1137/0515056
- I. Daubechies, Orthonormal bases of compactly supported wavelets, Commun. Pure Appl. Math. 41 (1988), 909-996. https://doi.org/10.1002/cpa.3160410705
- S. G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989), 674-693. https://doi.org/10.1109/34.192463
- I. Daubechies, Ten lectures on wavelets, Society for industrial and applied mathematics, PA, USA, 1992. https://doi.org/10.1137/1.9781611970104
- N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Royal Soc. A 454 (1998), 903-995. https://doi.org/10.1098/rspa.1998.0193
- 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, Acta Obstetricia et Gynecologica Scandinavica 56 (1976), 247-253.
- 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, Reproductive Health 10 (2013), S2. https://doi.org/10.1186/1742-4755-10-S1-S2
- Mayo Clinic, Premature birth, 2021. https://www.mayoclinic.org/diseases-conditions/premature-birth/symptoms-causes/syc-20376730
- World Health Organization, Preterm birth, 2018. https://www.who.int/news-room/fact-sheets/detail/preterm-birth
- Centers for Disease Control and Prevention, Preterm birth, 2020. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pretermbirth.htm
- T. Y. Euliano, M. T. Nguyen, S. Darmanjian, S. P. McGorray, N. Euliano, A. Onkala, and A. R. Gregg, Monitoring uterine activity during labor: a comparison of 3 methods, Am. J. Obstetrics Gynecology 208 (2013), 66.e1-e6.
- K. Thijssen, M. Vlemminx, M. Westerhuis, J. P. Dieleman, M. B. Van der Hout-Van der Jagt, and S. G. Oei, Uterine monitoring techniques from patients' and users' perspectives, AJP Reports 8 (2018), e184-e191. https://doi.org/10.1055/s-0038-1669409
- F. Jager, S. Libensek, and K. Gersak, Characterization and automatic classification of preterm and term uterine records, PLoS ONE 13 (2018), e0202125. https://doi.org/10.1371/journal.pone.0202125
- C. Marque, J. M. Duchene, S. Leclercq, G. S. Panczer, and J. Chaumont, Uterine EHG processing for obstetrical monitoring, IEEE Trans. Biomed. Eng. 333 (1986), 1182-1187.
- C. Buhimschi, M. B. Boyle, and R. E. Garfield, Electrical activity of human uterus during pregnancy as recorded from the abdominal surface, Obstet. Gynecol. 90 (1997), 102-111. https://doi.org/10.1016/S0029-7844(97)83837-9
- I. Verdenik, M. Pajntar, and B. Leskosek, Uterine electrical activity as predictor of preterm birth in women with preterm contractions, Eur. J. Obstet. Gynecol. 95 (2001), 149-153. https://doi.org/10.1016/S0301-2115(00)00418-8
- W. L. Maner, R. E. Garfield, H. Maul, G. Olson, and G. Saade, Predicting term and preterm delivery with transabdominal uterine electromyography, Obstetrics Gynecology 101 (2003), 1254-1260.
- H. de Lau, C. Rabotti, H. P. Oosterbaan, M. Mischi, and G. S. Oei, Study protocol: PoPE-prediction of preterm delivery by electrohysterography, BMC Pregnancy Childbirth 14 (2014), 192. https://doi.org/10.1186/1471-2393-14-192
- C. Rabotti and M. Mischi, Propagation of electrical activity in uterine muscle during pregnancy: a review, Acta Physiologica 213 (2015), 406-416. https://doi.org/10.1111/apha.12424
- H. Leman, C. Marque, and J. Gondry, Use of the electrohysterogram signal for characterization of contractions during pregnancy, IEEE Trans. Biomed. Eng. 46 (1999), 1222-1229. https://doi.org/10.1109/10.790499
- W. L. Maner and R. E. Garfield, Identification of human term and preterm labor using artificial neural networks on uterine electromyography data, Ann. Biomed. Eng. 35 (2007), 465-473. https://doi.org/10.1007/s10439-006-9248-8
- M. Lucovnik, W. L. Maner, L. R. Chambliss, R. Blumrick, J. Balducci, Z. Novak-Antolic, and R. E. Garfield, Noninvasive uterine electromyography for prediction of preterm delivery, Am. J. Obstetrics Gynecology 204 (2011), 228.e1-10.
- C. K. Marque, J. Terrien, S. Rihana, and G. Germain, Preterm labour detection by use of a biophysical marker: the uterine electrical activity, BME Pregnancy Childbirth 7 (2007), S5. https://doi.org/10.1186/1471-2393-7-S1-S5
- M. W. C. Vlemminx, K. M. J. Thijssen, G. I. Bajlekov, J. P. Dieleman, M. B. Van der Hout-Van der Jagt, and S. G. Oei, Electrohysterography for uterine monitoring during term labour compared to external tocodynamometry and intra-uterine pressure catheter, Eur. J. Obstetrics Gynecol. Reprod. Biol. 215 (2017), 197-205. https://doi.org/10.1016/j.ejogrb.2017.05.027
- C. Hemthanon, and S. Janjarasjitt, Correlation between time-domain features of electrohysterogram data of pregnant women and gestational age. In: K. P. Lin, R. Magjarevic, and P. de Carvalho (eds.), Future trends in biomedical and health informatics and cybersecurity in medical devices. vol. 74, Springer, Cham, 2020.
- J. Peng, D. Hao, L. Yang, M. Du, X. Song, H. Jiang, Y. Zhang, and D. Zheng, Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest, Biocybern. Biomed. Eng. 40 (2020), 352-362. https://doi.org/10.1016/j.bbe.2019.12.003
- U. R. Acharya, V. K. Sudarshan, S. Q. Rong, Z. Tan, C. M. Lim, J. E. Koh, S. Nayak, and S. Bhandary, Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals, Comput. Bio. Med. 85 (2017), 33-42. https://doi.org/10.1016/j.compbiomed.2017.04.013
- S. Janjarasjitt, Preterm-term birth classification using EMD-based time-domain features of single-channel electrohysterogram data, Phys. Eng. Sci. Med. 44 (2021), 1151-1159. https://doi.org/10.1007/s13246-021-01051-w
- J. Mas-Cabo, Y. Ye-Lin, J. Garcia-Casado, A. Diaz-Martinez, A. Perales-Marin, R. Monfort-Ortiz, A. Roca-Prats, A. Lopez- Corral, and G. Prats-Boluda, Robust characterization of the uterine myoelectrical activity in different obstetric scenarios, Entropy 22 (2020), 743. https://doi.org/10.3390/e22070743
- G. FeleZorz, G. Kavsek, Z. Novak-Antolic, and F. Jager, A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and preterm delivery groups, Med. Biol. Eng. Comput. 46 (2008), 911-922. https://doi.org/10.1007/s11517-008-0350-y
- P. Fergus, P. Cheung, A. Hussain, D. Al-Jumeily, C. Dobbins, and S. Iram, Prediction of preterm deliveries from EHG signals using machine learning, PLoS ONE 8 (2013), e77154. https://doi.org/10.1371/journal.pone.0077154
- P. Fergus, I. Idowu, A. Hussain, and C. Dobbins, Advanced artificial neural network classification for detecting preterm births using EHG records, Neurocomput. 188 (2016), 42-49. https://doi.org/10.1016/j.neucom.2015.01.107
- C. Hemthanon and S. Janjarasjitt, Examination of time-domain features of EHG data for preterm-term birth classification, J. Comput. 30 (2019), 41-54.
- D. Alamedine, M. Khalil, and C. Marque, Comparison of different EHG feature selection methods for the detection of preterm labor, Computat. Math. Methods Med. 2013 (2013). https://doi.org/10.1155/2013/485684
- A. Diab, M. Hassan, C. Marque, and B. Karlsson, Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals, Med. Eng. Phys. 36 (2014), 761-767. https://doi.org/10.1016/j.medengphy.2014.01.009
- M. Hassan, J. Terrien, C. Marque, and B. Karlsson, Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals, Med. Eng. Phys. 33 (2011), 980-986. https://doi.org/10.1016/j.medengphy.2011.03.010
- A. Smrdel and F. Jager, Separating sets of term and pre-term uterine EMG records, Physiol. Meas. 36 (2015), 341-355. https://doi.org/10.1088/0967-3334/36/2/341
- S. Janjarasjitt, Examination of single wavelet-based features of EHG signals for preterm birth classification, IAENG Int. J. Comput. Sci. 44 (2017), 212-218.
- S. Janjarasjitt, Evaluation of performance on preterm birth classification using single wavelet-based features of EHG signals, (Biomedical Engineering International Conference, Hokkaido, Japan), 2017, pp. 1-4. https://doi.org/10.1109/BMEiCON.2017.8229118
- B. Moslem, M. Diab, M. Khalil, and C. Marque, Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals, EURASIP J. Adv. Signal Process. 2012 (2012), 167. https://doi.org/10.1186/1687-6180-2012-167
- P. Ren, S. Yao, J. Li, P. A. Valdes-Sosa, and K. M. Kendrick, Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals, PLoS ONE 10 (2015), e0132116. https://doi.org/10.1371/journal.pone.0132116
- L. Chen and Y. Hao, Feature extraction and classification of EHG between pregnancy and labour group using Hilbert-Huang transform and extreme learning machine, Computat. Math. Methods Med. 2017 (2017), https://doi.org/10.1155/2017/7949507
- F. J. Ferri, P. Pudil, M. Hatef, and J. Kittler, Comparative study of techniques for large-scale feature selection, Mach. Intell. Pattern Recogn. 16 (1994), 403-413.
- P. Pudil, J. Novovicova, and J. Kittler, Floating search methods in feature selection, Pattern Recogn. Lett. 15 (1994), 1119-1125. https://doi.org/10.1016/0167-8655(94)90127-9
- S. Mallat, A wavelet tour of signal processing, 1st ed., Academic Press, San Diego, 1998.
- M. Vetterli and C. Herley, Wavelets and filter banks: theory and design, IEEE Trans. Signal Process. 40 (1992), 2207-2232. https://doi.org/10.1109/78.157221
- R. Fontugne, P. Borgnat, and P. Flandrin, Online empirical mode decomposition, (IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, LA, USA), Mar. 2017, pp. 4306-4310.
- N. E. Huang and S. S. Shen, Hilbert-Huang transform and its applications, 2nd ed., World Scientific, New Jersey, 2014.
- N. E. Huang, M.-L. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen, and K. L. Fan, A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proc. Royal Soc. A 459 (2003), 2317-2345. https://doi.org/10.1098/rspa.2003.1123
- F. Jager, Term-Preterm EHG Database, 2012. https://www.physionet.org/content/tpehgdb/1.0.1/
- A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals, Circulation 101 (2000), no. 23, e215-e220.
- K. S. Kim, H. H. Choi, C. S. Moon, and C. W. Mun, Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions, Current Appl. Phys. 11 (2011), 740-745. https://doi.org/10.1016/j.cap.2010.11.051
- M. A. Oskoei and H. Hu, Support vector machine-based classification scheme for myoelectric control applied to upper limb, IEEE Trans. Biomed. Eng. 55 (2008), 1956-1965. https://doi.org/10.1109/TBME.2008.919734
- D. Tkach, H. Huang, and T. A. Kuiken, Study of stability of time-domain features for electromyographic pattern recognition, J. NeuroEng. Rehabilitation 7 (2010), 21. https://doi.org/10.1186/1743-0003-7-21
- M. Zardoshti-Kermani, B. C. Wheeler, K. Badie, and R. M. Hashemi, EMG feature evaluation for movement control of upper extremity prostheses, IEEE Trans. Rehabilitation Eng. 3 (1995), 324-333. https://doi.org/10.1109/86.481972
- I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res. 3 (2003), 1157-1182.
- T. Fawcett, An introduction to ROC analysis, Pattern Recogn. Lett. 27 (2006), 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
- A. M. Kaleem and R. D. Kokate, Prediction of pre-term groups from EHG signals using optimal multi-kernel SVM, J. Ambient Intell. Humanized Comput. 12 (2021), 3689-3703. https://doi.org/10.1007/s12652-019-01648-w
- A. J. Hussain, P. Fergus, H. Al-Askar, D. Al-Jumeily, and F. Jager, Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women, Neurocomput. 151 (2015), 963-974. https://doi.org/10.1016/j.neucom.2014.03.087
- H. He, Y. Bai, E. A. Garcia, and S. Li, ADASYN: adaptive synthetic sampling approach for imbalanced learning, (IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong), 2008, pp. 1322-1328.