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. 2022 Aug 18:8:e1050.
doi: 10.7717/peerj-cs.1050. eCollection 2022.

Intrapartum cardiotocography trace pattern pre-processing, features extraction and fetal health condition diagnoses based on RCOG guideline

Shahad Al-Yousif  1   2   3 , Ihab A Najm  4 , Hossam Subhi Talab  5 , Nourah Hasan Al Qahtani  6 , M Alfiras  2 , Osama Ym Al-Rawi  2 , Wisam Subhi Al-Dayyeni  1 , Ali Amer Ahmed Alrawi  7 , Mohannad Jabbar Mnati  8 , Mu'taman Jarrar  9 , Fahad Ghabban  10 , Nael A Al-Shareefi  11 , Mustafa Musa Jaber  12   13 , Abbadullah H Saleh  14 , Nooritawati Md Tahir  15   16   17 , Huda T Najim  18 , Mayada Taher  19
Affiliations

Intrapartum cardiotocography trace pattern pre-processing, features extraction and fetal health condition diagnoses based on RCOG guideline

Shahad Al-Yousif et al. PeerJ Comput Sci. .

Abstract

Context: The computerization of both fetal heart rate (FHR) and intelligent classification modeling of the cardiotocograph (CTG) is one of the approaches that are utilized in assisting obstetricians in conducting initial interpretation based on (CTG) analysis. CTG tracing interpretation is crucial for the monitoring of the fetal status during weeks into the pregnancy and childbirth. Most contemporary studies rely on computer-assisted fetal heart rate (FHR) feature extraction and CTG categorization to determine the best precise diagnosis for tracking fetal health during pregnancy. Furthermore, through the utilization of a computer-assisted fetal monitoring system, the FHR patterns can be precisely detected and categorized.

Objective: The goal of this project is to create a reliable feature extraction algorithm for the FHR as well as a systematic and viable classifier for the CTG through the utilization of the MATLAB platform, all the while adhering to the recognized Royal College of Obstetricians and Gynecologists (RCOG) recommendations.

Method: The compiled CTG data from spiky artifacts were cleaned by a specifically created application and compensated for missing data using the guidelines provided by RCOG and the MATLAB toolbox after the implemented data has been processed and the FHR fundamental features have been extracted, for example, the baseline, acceleration, deceleration, and baseline variability. This is followed by the classification phase based on the MATLAB environment. Next, using the guideline provided by the RCOG, the signals patterns of CTG were classified into three categories specifically as normal, abnormal (suspicious), or pathological. Furthermore, to ensure the effectiveness of the created computerized procedure and confirm the robustness of the method, the visual interpretation performed by five obstetricians is compared with the results utilizing the computerized version for the 150 CTG signals.

Results: The attained CTG signal categorization results revealed that there is variability, particularly a trivial dissimilarity of approximately (+/-4 and 6) beats per minute (b.p.m.). It was demonstrated that obstetricians' observations coincide with algorithms based on deceleration type and number, except for acceleration values that differ by up to (+/-4).

Discussion: The results obtained based on CTG interpretation showed that the utilization of the computerized approach employed in infirmaries and home care services for pregnant women is indeed suitable.

Conclusions: The classification based on CTG that was used for the interpretation of the FHR attribute as discussed in this study is based on the RCOG guidelines. The system is evaluated and validated by experts based on their expert opinions and was compared with the CTG feature extraction and classification algorithms developed using MATLAB.

Keywords: Cardiotocograph; Electronic fetal monitoring; Fetal heart rate; Uterine contraction.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Information on various CTG trace patterns.
Figure 2
Figure 2. Overall procedures for feature extraction and classification system.
Figure 3
Figure 3. Baseline algorithm procedures.
Figure 4
Figure 4. FHR signal with the virtual baseline value R.
Figure 5
Figure 5. New signal limitations in terms of accuracy, where α = 1, 2, 3 … 15.
Figure 6
Figure 6. Limitations of algorithms.
Figure 7
Figure 7. FHR signal that has been truncated.
Figure 8
Figure 8. Real baseline BL & FHR signal.
Figure 9
Figure 9. When X and Y are at least 15 s and 15 b.p.m., respectively, transient increases in the FHR reflect acceleration.
Figure 10
Figure 10. Algorithm for determining acceleration.
Figure 11
Figure 11. Periods of FHR acceleration and points where it intersects with the baseline.
Figure 12
Figure 12. Calculation of FHR variability over a 2-min cycle.
Figure 13
Figure 13. Variability time in 2 s.
Figure 14
Figure 14. Procedure for detecting deceleration.
Figure 15
Figure 15. Periods of FHR deceleration and points where it intersects with the baseline.
Figure 16
Figure 16. FHR signal & uterine contractions.
Figure 17
Figure 17. Identification of different modes of deceleration.
Figure 18
Figure 18. Deceleration time.
Figure 19
Figure 19. Deceleration vs uterine contraction time gap.
Figure 20
Figure 20. Procedure for classification in general.
Figure 21
Figure 21. (A) The noisy FHR signal, (B) the denoised FHR signal with w = 1, (C) w = 5, (D) w = 10, (E) w = 20, (F) w = 30, (G) w = 50, (H) w = 100.
Figure 22
Figure 22. A sample of FHR and UC (A) before pre-processing, (B) after pre-processing.
Figure 23
Figure 23. Comparisons between computerized baseline estimation and expert estimation.
Figure 24
Figure 24. Baseline FHR results for synthetic CTG signals are computerized and visually estimated.
Figure 25
Figure 25. Baseline FHR outcomes for clinical CTG signals are computerized and visually estimated.
Figure 26
Figure 26. FHR baseline variability effects for semi-synthetic CTG signals: computerized and visual estimation.
Figure 27
Figure 27. FHR baseline variability outcomes for clinical CTG signals: computerized and visual estimation.
Figure 28
Figure 28. Number of accelerations for 50 semi-synthetic signals.
Figure 29
Figure 29. Number of acceleration results for 50 clinical signals.
Figure 30
Figure 30. Number of decelerations for 50 synthetic signals.
Figure 31
Figure 31. Number of clinical signal deceleration findings.
Figure 32
Figure 32. CTG classification findings using a rule-based method for 50 synthetic signals.
Figure 33
Figure 33. Fuzzy logic CTG classification findings for 50 clinical signals.
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References

    1. Akhavan-Amjadi M. Fetal electrocardiogram modeling using hybrid evolutionary firefly algorithm and extreme learning machine. Multidimensional Systems and Signal Processing. 2020;31(1):117–133. doi: 10.1007/s11045-019-00653-8. - DOI
    1. Al-Yousif S, Jaenu A, Al-Dayyeni W, Alamoodi A, Najm IA, Md Tahir N, Alrawi AAA, Cömert Z, Al-shareefi NA, Saleh AH. A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science. 2021;7:e452. doi: 10.7717/peerj-cs.452. - DOI - PMC - PubMed
    1. Alsaggaf W, Cömert Z, Nour M, Polat K, Brdesee H, Toğaçar M. Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals. Applied Acoustics. 2020;167:107429. doi: 10.1016/j.apacoust.2020.107429. - DOI
    1. Alsayyari A. Fetal cardiotocography monitoring using Legendre neural networks. Biomedical Engineering/Biomedizinische Technik. 2019;64(6):669–675. doi: 10.1515/bmt-2018-0074. - DOI - PubMed
    1. Ayres-de-campos D, Bernardes J, Garrido A, Marques-de-sá J, Pereira-leite L. SisPorto 2.0: a program for automated analysis of cardiotocograms. Journal of Maternal-Fetal Medicine. 2000;9(5):311–318. doi: 10.3109/14767050009053454. - DOI - PubMed

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