Classification of Term and Preterm Birth Data from Elektrohisterogram (EHG) Data by Empirical Wavelet Transform Based Machine Learning Methods

Öz

Accurate prediction of preterm birth can significantly reduce birth complications for both mother and baby. This situation increases the need for an effective technique in early diagnosis. Therefore, machine learning methods and techniques used on Electrohysterogram (EHG) data are increasing day by day. The aim of this study is to evaluate the effectiveness of the Empirical Wavelet Transform (EWT) approach on EHG data and to propose an algorithm for estimating preterm birth using single EHG signal. The data used in the study were taken from Physionet's Term-Preterm Electrohysterogram Database (TPEHGDB) and scored in one-minute windows. The feature matrix was obtained by calculating the sample entropy value from each of the discretized EHG modes obtained as a result of this method, which was used for the first time on EHG data, and the average energy value from the signal obtained by recombining the modes. The obtained features were applied to Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM) algorithms to predict preterm birth. Among the classifier algorithms, the RF algorithm achieved the best result with a success rate of 98,20%.

Anahtar Kelimeler

• Classification
• Electrohysterogram
• Empirical Wavelet Transform

Kaynakça

1. [1] P. Gondane, S. Kumbhakarn, P. Maity, K. Kapat. “Recent Advances and Challenges in the Early Diagnosis and Treatment of Preterm Labor.” Bioengineering, 2024. https://doi.org/10.3390/bioengineering11020161.
2. [2] M. Delnord, J. Zeitlin. “Epidemiology of late preterm and early term births – An international perspective.” Seminars in Fetal and Neonatal Medicine, 2019. https://doi.org/10.1016/j.siny.2018.09.001.
3. [3] J. Xu, Z. Chen, J. Zhang, Y. Lu, X. Yang, A. Pumir. “Realistic preterm prediction based on optimized synthetic sampling of EHG signal.” Computers in Biology and Medicine, 2021. https://doi.org/10.1016/j.compbiomed.2021.104644.
4. [4] J. Peng, D. Hao, L. Yang, M. Du, X. Song, H. Jiang, Y. Zhang, D. Zheng. “Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest.” Biocybernetics and Biomedical Engineering, 2019. https://doi.org/10.1016/j.bbe.2019.12.003.
5. [5] C. Gao, S. Osmundson, D.R.V. Edwards, G.P. Jackson, B.A. Malin, Y. Chen. “Deep learning predicts extreme preterm birth from electronic health records.” Journal of Biomedical Informatics, 2019. https://doi.org/10.1016/j.jbi.2019.103334.
6. [6] H.H. Chang, J. Larson, et al. “Preventing preterm births: analysis of trends and potential reductions with interventions in 39 countries with very high human development index.” The Lancet, 2013. https://doi.org/10.1016/S0140-6736(12)61856-X.
7. [7] J.A. Mccoshen, P.A. Fernandes, M.L. Boroditsky, J.G. Allardice. “Determinants of reproductive mortality and preterm childbirth. In: Bittar EE, Zakar T (ed) Advances in Organ Biology.” Elsevier, 1996, pp 195-223.
8. [8] M. Shahrdad, M.C. Amirani. “Detection of preterm labor by partitioning and clustering the EHG signal.” Biomedical Signal Processing and Control, 2018. https://doi.org/10.1016/j.bspc.2018.05.044.

9. [9] P. Fergus, I. Idowu , A. Hussain, C. Dobbins. “Advanced artificial neural network classification for detecting preterm births using EHG records.” Neurocomputing, 2015. https://doi.org/10.1016/j.neucom.2015.01.107.
10. [10] S. Vinothini, N. Punitha, P.A. Karthick, S. Ramakrishnan. “Automated detection of preterm condition using uterine electromyography based topological features.” Biocybernetics and Biomedical Engineering, 2021. https://doi.org/10.1016/j.bbe.2021.01.004.
11. [11] S.M. Far, M. Beiramvand, M. Shahbakhti, P. Augustyniak. “Prediction of preterm delivery from unbalanced EHG database.” Sensors, 2022. https://doi.org/10.3390/s22041507.
12. [12] H. Lou, H. Liu, Z. Chen, Z. Zhen, B. Dong, J. Xu. “Bio-process inspired characterization of pregnancy evolution using entropy and its application in preterm birth detection.” Biomedical Signal Processing and Control, 2022. https://doi.org/10.1016/j.bspc.2022.103587
13. [13] 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, 2015. https://doi.org/10.1016/j.neucom.2014.03.087
14. [14] J. Xu, Z. Chen, H. Lou, G. Shen, A. Pumir. “Review on EHG signal analysis and its application in preterm diagnosis.” Biomedical Signal Processing and Control, 2022. https://doi.org/10.1016/j.bspc.2021.103231
15. [15] E. Nsugbe, “Novel uterine contraction signals decomposition for enhanced preterm and birth imminency prediction.” Intelligent Systems with Applications, 2022. https://doi.org/10.1016/j.iswa.2022.200123
16. [16] K. B. E. Dine, N. Nader, M. Khalil, C. Marque. “Uterine synchronization analysis during pregnancy and labor using graph theory, classification based on neural network and deep learning.” IRBM, 2022. https://doi.org/10.1016/j.irbm.2021.09.002
17. [17] PhysioNet, The term–preterm EHG database (TPEHG-DB), (physionet.org), 2012.
18. [18] F. Jager, S. Libensek, K. Gersak. The term-preterm EHG dataset with tocogram (TPEHGTDS)[data set], 2018.
19. [19] R. Kumar, I. Saini. “Empirical wavelet transform based ECG signal compression.” IETE Journal of Research, 2014. https://doi.org/10.1080/03772063.2014.963173.
20. [20] C. K. Jha, M. H. Kolekar. “Empirical mode decomposition and wavelet transform based ECG data compression scheme.” IRBM, 2021. https://doi.org/10.1016/j.irbm.2020.05.008.
21. [21] A. Anuragi, D. S. Sisodia. “Empirical wavelet transform based automated alcoholism detecting using EEG signal features.” Biomedical Signal Processing and Control, 2020. https://doi.org/10.1016/j.bspc.2019.101777.
22. [22] M. Almeida, H. Mourino H, A. G. Batista, S. Russo, F. Esgalhado, C. R. P. Reis, F. Serrano, M. Ortigueira. “Electrohysterography extracted features dependency on anthropometric and pregnancy factors.” Biomedical Signal Processing and Control, 2022. https://doi.org/10.1016/j.bspc.2022.103556.
23. [23] Y. H. Wang, I. Y. Chen, H. Chiueh, S. F. Liang, “A low-cost implementation of sample entropy in wearable embedded systems: an example of online analysis for sleep EEG.” in IEEE Transactions on Instrumentation and Measurement, 2021. https://doi.org/10.1109/TIM.2020.3047488.
24. [24] D. C. Dickin, R. K. Surowiec, H. Wang. “Energy expenditure and muscular activation patterns through active sitting on compliant surfaces.” Journal of Sport and Health Science, 2017. https://doi.org/10.1016/j.jshs.2015.10.004.
25. [25] E. Tuncer, E. D. Bolat, “Classification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (Bi-LSTM) network architecture.” Biomedical Signal Processing and Control, 2022. https://doi.org/10.1016/j.bspc.2021.103462.
26. [26] Y. Mohia, F. Ouallouch, M. Lazri et al. “Classification of precipitation ıntensities from remote sensing data based on artificial ıntelligence using RF multi-learning." J. Indian Soc. Remote Sens., 2023. https://doi.org/10.1007/s12524-023-01665-5.
27. [27] E. Celik, T. Dal, T. Aydın. “Comparison of data mining classification algorithms for sentiment analysis.” European Journal of Science and Technology Vol.27, 880-889, 2021.
28. [28] V. Srivardhan. “Adaptive boosting of random forest algorithm for automatic petrophysical interpretation of well logs.” Acta Geodaetica et Geophysica, 2022. https://doi.org/10.1007/s40328-022-00385-5.
29. [29] D. R. Edla, K. Mangalorekar, G. Dhavalikar, S. Dodia. “Classification of EEG data for human mental state analysis using random forest classifier.” Procedia Computer Science, 2018. https://doi.org/10.1016/j.procs.2018.05.116.
30. [30] E. Tuncer, E. D. Bolat, “Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques.” Biocybernetics and Biomedical Engineering, 2022. https://doi.org/10.1016/j.bbe.2022.04.004.
31. [31] K. M. Sunnetci, A. Alkan. “KNN and decision trees based SPPM demodulators applicable to synchronous modulation techniques.” Journal of the Faculty of Engineering and Architecture of Gazi University, 2022. https://doi.org/10.17341/gazimmfd.890721.
32. [32] J. Hu, H. Peng, J. Wang, W. Yu. “kNN-P: A kNN classifier optimized by P systems.” Theoretical Computer Science, 2020. https://doi.org/10.1016/j.tcs.2020.01.001.
33. [33] O. F. Ertugrul, M. E. Tagluk. “A novel version of k nearest neighbor: Dependent nearest neighbor.” Applied Soft Computing, 2017. https://doi.org/10.1016/j.asoc.2017.02.020.
34. [34] S. Tanisman, A. A. Karcioglu, A. Ugur, H. Bulut. “Forecasting of bitcoin price using LSTM neural network and ARIMA time series models and comparision of methods.” European Journal of Science and Technology, 32:514-520, 2021.
35. [35] K. M. Chinea, J. Ortega, J. F. Gomez-Gonzalez et al. “Effect of time windows in LSTM networks for EEG-based BCIs.” Cogn Neurodyn, 2022. https://doi.org/10.1007/s11571-022-09832-z.
36. [36] S. Ruuska, W. Hamalainen, S. Kajava, M. Mughal, P. Matilainen, J. Mononen. “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle.” Behavioural Processes, 2018. https://doi.org/10.1016/j.beproc.2018.01.004.
37. [37] J. Yoo, I. Yoo, I. Youn, et al. “Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability.” Computer Methods and Programs in Biomedicine, 2022. https://doi.org/10.1016/j.cmpb.2022.107079.
38. [38] Z. Peng et al. “A continuous late-onset sepsis prediction algorithm for preterm ınfants using multi-channel physiological signals from a patient monitor.” in IEEE Journal of Biomedical and Health Informatics, 2023. https://doi.org/10.1109/JBHI.2022.3216055.
39. [39] X. Song, X. Qiao, D. Hao, L. Yang, X. Zhou, Y. Xu, D. Zheng. “Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate.” Sci Rep., 2021. https://doi.org/10.1038/s41598-021-81492-1.