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

Shahad Al-Yousif; Ihab A Najm; Hossam Subhi Talab; Nourah Hasan Al Qahtani; M Alfiras; Osama Ym Al-Rawi; Wisam Subhi Al-Dayyeni; Ali Amer Ahmed Alrawi; Mohannad Jabbar Mnati; Mu'taman Jarrar; Fahad Ghabban; Nael A Al-Shareefi; Mustafa Musa Jaber; Abbadullah H Saleh; Nooritawati Md Tahir; Huda T Najim; Mayada Taher

doi:10.7717/peerj-cs.1050

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

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

Authors

Shahad Al-Yousif^{1

2

3}, Ihab A Najm⁴, Hossam Subhi Talab⁵, Nourah Hasan Al Qahtani⁶, M Alfiras², Osama Ym Al-Rawi², Wisam Subhi Al-Dayyeni¹, Ali Amer Ahmed Alrawi⁷, Mohannad Jabbar Mnati⁸, Mu'taman Jarrar⁹, Fahad Ghabban¹⁰, Nael A Al-Shareefi¹¹, Mustafa Musa Jaber^{12

13}, Abbadullah H Saleh¹⁴, Nooritawati Md Tahir^{15

16

17}, Huda T Najim¹⁸, Mayada Taher¹⁹

Affiliations

¹ Research Centre, The University of Almashreq, Baghdad, Iraq.
² College of Engineering, Department of Electrical & Electronic Engineering, Gulf University, Almasnad, Kingdom of Bahrain.
³ Department of Medical Instrumentation Engineering Techniques, Dijlah University College, Baghdad, Iraq.
⁴ College of Engineering, Tikrit University, Tikrit, Iraq.
⁵ Children Welfare Teaching Hospital, Medical City, (MD, CABP, CAB Neonatology), Baghdad, Iraq.
⁶ Department of Obstetrics and Gynecology, College of Medicine, Imam Abdulrahman Bin Faisal University, Al Dammam, Saudi Arabia.
⁷ Faculty of Engineering, Alanbar University, Alanbar, Iraq.
⁸ Department of Electronic Technology, Institute of Technology Baghdad, Middle Technical University, Baghdad, Iraq.
⁹ College of Medicine, Imam Abdulrahman Bin Faisal University, Al Dammam, Saudi Arabia.
¹⁰ Department of Information Systems College of Computer Science and Engineering, Taibah University, Al Madinah Al Munawwarah, Saudi Arabia.
¹¹ College of Biomedical Informatics, University of Information Technology and Communications (UOITC), Baghdad, Iraq.
¹² Al-Turath University College, Department of Computer Engineering, Baghdad, Iraq.
¹³ Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, Iraq.
¹⁴ Department of Computer Engineering, Karabük University, Karabük, Turkey.
¹⁵ Electrical Engineering Department, College of Engineering, Universiti Teknologi MARA, Shah Alam, Malaysia.
¹⁶ Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA, Shah Alam, Malaysia.
¹⁷ Institute of Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Malaysia.
¹⁸ Department of Biomedical Engineering, University of Technology, Baghdad, Iraq.
¹⁹ Department of Laser and Optoelectronics Engineering, University of Technology, Baghdad, Iraq.

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.

Grants and funding

The authors received no funding for this work.