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

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.

Determining the optimal hematoma volume-based thresholds for surgical and medical strategies in basal ganglia hemorrhage

“…Consequently, our study highlights hematoma volume as the primary indicator for surgery and conservative treatment, aligning with Abino Luzzi et al's systematic reviews, which specify that the surgical volume threshold is at least 60 ml. (Table .6) Currently, machine learning methods such as artificial intelligence (AI) and deep learning are crucial for detecting and classifying intracranial hemorrhage (26,27,28) , facilitating early identification and improving diagnostic precision. In the future, AI technologies have the potential to expand capabilities globally, with this study aiding physicians in comprehending prognosis, post-treatment outcomes and facilitating prompt decision-making regarding initial management at healthcare facilities.…”

“…Currently, machine learning methods such as artificial intelligence (AI) and deep learning are crucial for detecting and classifying intracranial hemorrhage (26, 27, 28) , facilitating early identification and improving diagnostic precision. In the future, AI technologies have the potential to expand capabilities globally, with this study aiding physicians in comprehending prognosis, post-treatment outcomes and facilitating prompt decision-making regarding initial management at healthcare facilities.…”