Intelligent fetal heart rate analysis

Skinner, Jonathan F.; Garibaldi, Jonathan M.; Curnow, J.S.K.; Ifeachor, Emmanuel

doi:10.1049/cp:20000311

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…The use of fuzzy inference systems in predicting fetal distress based on fetal heart rate has been explored in various studies. Skinner et al introduced a fuzzy system-based classifier that categorised CTG segments as normal, intermediate, abnormal, or severely abnormal [200]. Similarly, Huang et al developed a monitoring system based on fuzzy inference for diagnosing non-reassuring fetal status using FHR and uterine pressure data [201].…”

Section: Classification and Performance Evaluationmentioning

confidence: 99%

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities

Hussain,

O’Halloran,

McDermott

et al. 2024

Biomed. Phys. Eng. Express

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Intrapartum foetal hypoxia is related to long-term morbidity and mortality of the foetus and the mother. Foetal surveillance is extremely important to minimize the adverse outcomes arising from foetal hypoxia during labour. Several methods have been used in current clinical practice to monitor foetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum foetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including foetal scalp blood sampling and foetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from foetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during foetal hypoxia to foetal monitoring technologies. The objective of this article is to assess the clinical background of foetal hypoxia and to review existing monitoring technologies for the detection and monitoring of foetal hypoxia. A comprehensive review has been made to predict foetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous foetal monitoring and may result in the accurate detection of intrapartum foetal hypoxia.

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Section: Classification and Performance Evaluationmentioning

confidence: 99%

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities

Hussain,

O’Halloran,

McDermott

et al. 2024

Biomed. Phys. Eng. Express

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“…Neural Networks can be trained and learn to detect any key feature of CTG such as acceleration, contractions and etc. [15] [16].…”

Section: Cardiotocogrammentioning

confidence: 99%

The challenge of using soft computing for decision support during labour

Stylios¹,

Georgoulas²,

Groumpos³

2001

PsycEXTRA Dataset

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Abstract-This paper presents the development of a Decision Support System for managing the labour based on the Soft Computing technique of Fuzzy Cognitive Maps. During labour, obstetricians manage and take decisions on the fetus delivery and the procedure of the labour, monitoring continuously a visual recording of the fetus heart signal and uterine contractions and they also take under consideration other physiological medical measurements and factors. Obstetricians utilize their experience and they decide either to proceed to a natural labour or to a Caesarean section. The developing system for managing labour delivery will combine evaluation of cardiotocographic signal with other physiological data in order to create an advanced Decision Support System.

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“…First, there are significant, inherent problems of imprecision and uncertainty in the clinical data and the interpretation methods used [11]. These problems have yet to be addressed in computerized CTG systems.…”

Section: Introductionmentioning

confidence: 99%

Models for handling uncertainty in fetal heart rate and ECG analysis

Ifeachor

Curnow²,

Outram³

et al.

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Methods for handling imprecision and uncertainty in computer-based analysis of fetal heart rate patterns and ECG wave shape during childbirth are presented. Computational intelligence models, based on fuzzy logic techniques, that explicitly handle the imprecision and uncertainty inherent in the data obtained during childbirth and methods of interpreting the data are proposed. The ability to handle imprecision and uncertainty in clinical data and method of interpretation is vital to remove a key obstacle in electronic fetal monitoring.Keywords -Electronic fetal monitoring, fetal heart rate, cardiotocogram, fetal ECG, fuzzy logic, computational intelligence models. I. INTRODUCTIONMethods for handling imprecision and uncertainty in computer-based analysis of fetal heart rate patterns and electrocardiogram (ECG) wave shape during labour are described. Childbirth is a critical period for the fetus and mother. The outcome of labour is normally good, but sometimes problems occur that may lead to injury (e.g. fetal brain damage) or even death [1,2].Electronic fetal monitoring, introduced in the late 1960's [3], was expected to improve patient care, but this has not yet happened. The most common monitoring method is based on a continuous trace of the fetal heart rate pattern and maternal contractions, known as the cardiotocogram (CTG). Difficulties in the interpretation of the CTG have led to unnecessary medical interventions (e.g. Caesarean sections and forcept deliveries)[2] and a failure to intervene when necessary [1] (which can lead to preventable injuries and deaths). These problems have led to the development of a number of computerized systems to assist with the analysis and interpretation of CTG [4][5][6][7][8][9][10]. However, despite over two decades of development no system is in widespread routine clinical practice.Progress in computerized CTG analysis has been impeded by several factors. First, there are significant, inherent problems of imprecision and uncertainty in the clinical data and the interpretation methods used [11]. These problems have yet to be addressed in computerized CTG systems. Secondly, the CTG does not contain sufficient information for accurate assessment of the fetal condition [12]. Additional information may be obtained by a proper analysis of changes in the fetal electrocardiogram (ECG), but the problems of uncertainty and imprecision also exist in fetal ECG analysis.We have proposed computational intelligence models, based on fuzzy logic techniques, to explicitly handle the imprecision and uncertainty in clinical knowledge and data.In this paper, we describe the models and their application to fetal heart rate and ECG analysis. In sections II and III, respectively, the development of the models for CTG and ECG analysis will be presented. In each case, we start with a highlight of the nature and sources of imprecision and uncertainty to provide the basis for designing the fuzzy models. II. MODELLING UNCERTAINTY AND IMPRECISION IN FETAL HEART RATE ANALYSIS A. Uncertainty and i...

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Intelligent fetal heart rate analysis

Cited by 10 publications

References 8 publications

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities

The challenge of using soft computing for decision support during labour

Models for handling uncertainty in fetal heart rate and ECG analysis

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