AIMC Topic: Fetal Monitoring
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TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.
IEEE journal of biomedical and health informatics
May 6, 2025
Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged by maternal ECG (MECG) and background noise in abdominal ECG (AECG), it is challenging to exactly re...
Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer.
Computers in biology and medicine
Mar 16, 2025
Assessing fetal health traditionally involves techniques like echocardiography, which require skilled professionals and specialized equipment, making them unsuitable for low-resource settings. An emerging alternative is Phonocardiography (PCG), which...
A multimodal deep learning-based algorithm for specific fetal heart rate events detection.
Biomedizinische Technik. Biomedical engineering
Nov 4, 2024
OBJECTIVES: This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being.
A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.
Interdisciplinary sciences, computational life sciences
Oct 5, 2024
Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeu...
Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals.
Scientific reports
Jun 1, 2024
Standard clinical practice to assess fetal well-being during labour utilises monitoring of the fetal heart rate (FHR) using cardiotocography. However, visual evaluation of FHR signals can result in subjective interpretations leading to inter and intr...
Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture.
Sensors (Basel, Switzerland)
May 6, 2024
The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FE...
Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning.
American journal of obstetrics and gynecology
Apr 24, 2024
BACKGROUND: Electronic fetal monitoring is used in most US hospital births but has significant limitations in achieving its intended goal of preventing intrapartum hypoxic-ischemic injury. Novel deep learning techniques can improve complex data proce...
Deep learning with fetal ECG recognition.
Physiological measurement
Nov 27, 2023
Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG reco...
A CNN-RNN unified framework for intrapartum cardiotocograph classification.
Computer methods and programs in biomedicine
Dec 5, 2022
BACKGROUND AND OBJECTIVE: Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is very vital for pregnant women before delivery. During pregnancy, it is crucial to judge whether the fetus is abnormal, which helps obstetric...
AECG-DecompNet: abdominal ECG signal decomposition through deep-learning model.
Physiological measurement
May 10, 2021
The accurate decomposition of a mother's abdominal electrocardiogram (AECG) to extract the fetal ECG (FECG) is a primary step in evaluating the fetus's health. However, the AECG is often affected by different noises and interferences, such as the mat...
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