AIMC Topic: Cardiotocography

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Cardiotocography in Obstetrics: New Solutions for "Routine" Technology.

Sensors (Basel, Switzerland)
This work is devoted to the problems of one of the most common screening examinations used in medical practice: fetal cardiotocography (CTG). The technology of ultrasonic monitoring of fetal heart rate (HR) variations has been used for more than 70 y...

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals.

Sensors (Basel, Switzerland)
The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents in...

The present and future of intrapartum computerized cardiotocography: role of pattern recognition incorporating single vs. multiple parameters.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
Computer assisted cardiotocography holds a great promise in minimizing human errors thereby improving perinatal outcome. Despite exponential growth (Moore's law) in computing power for decades, this promise remains unrealized. The systematic analyses...

Intelligent Neutrosophic Diagnostic System for Cardiotocography Data.

Computational intelligence and neuroscience
Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The ...

Classifying the type of delivery from cardiotocographic signals: A machine learning approach.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiotocography (CTG) is the most employed methodology to monitor the foetus in the prenatal phase. Since the evaluation of CTG is often visual, and hence qualitative and too subjective, some automated methods have been int...

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network.

BMC medical informatics and decision making
BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in signif...

Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Intrauterine Growth Restriction (IUGR) is a fetal condition defined as the abnormal rate of fetal growth. The pathology is a documented cause of fetal and neonatal morbidity and mortality. In clinical practice, diagnosis is...

Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis.

Archives of gynecology and obstetrics
OBJECTIVES: To determine the degree of inter-rater reliability (IRR) between human and artificial intelligence (AI) interpretation of fetal heart rate tracings (FHR), and to determine whether AI-assisted electronic fetal monitoring interpretation imp...

Prediction of fetal state from the cardiotocogram recordings using neural network models.

Artificial intelligence in medicine
The combination of machine vision and soft computing approaches in the clinical decisions, using training data, can improve medical decisions and treatments. The cardiotocography (CTG) monitoring and uterine activity (UA) provides useful information ...