Computer analysis of antepartum fetal heart rate: 1. Baseline determination

Mantel, R.; Geijn, H.P. van; Caron, F.J.M.; Swartjes, J.M.; Woerden, E.E. van; Jongsma, H.W.

doi:10.1016/0020-7101(90)90030-x

Cited by 102 publications

(50 citation statements)

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“…Shape, intensity and frequency of UC, foetal movements, the average, variability, accelerations and decelerations of FHR are mainly evaluated [8,29,30]. In particular, during labour, FHR alterations in correspondence to UC are estimated to assess foetal reactivity.…”

Section: Introductionmentioning

confidence: 99%

Foetal heart rate power spectrum response to uterine contraction

et al. 2006

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Cardiotocography is the most diffused prenatal diagnostic technique in clinical routine. The simultaneous recording of foetal heart rate (FHR) and uterine contractions (UC) provides useful information about foetal well-being during pregnancy and labour. However, foetal electronic monitoring interpretation still lacks reproducibility and objectivity. New methods of interpretation and new parameters can further support physicians' decisions. Besides common time-domain analysis, study of the variability of FHR can potentially reveal autonomic nervous system activity of the foetus. In particular, it is clinically relevant to investigate foetal reactions to UC to diagnose foetal distress early. Uterine contraction being a strong stimulus for the foetus and its autonomic nervous system, it is worth exploring the FHR variability response. This study aims to analyse modifications of the power spectrum of FHR variability corresponding to UC. Cardiotocographic signal tracts corresponding to 127 UC relative to 30 healthy foetuses were analysed. Results mainly show a general, statistically significant (t test, p<0.01) power increase of the FHR variability in the LF 0.03-0.2 Hz and HF 0.2-1 in correspondence of the contraction with respect to a reference tract set before contraction onset. Time evolution of the power within these bands was computed by means of time-varying spectral estimation to concisely show the FHR response along a uterine contraction. A synchronised grand average of these responses was also computed to verify repeatability, using the contraction apex as time reference. Such modifications of the foetal HRV that follow a contraction can be a sign of ANS reaction and, therefore, additional, objective information about foetal reactivity during labour.

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Section: Introductionmentioning

confidence: 99%

Foetal heart rate power spectrum response to uterine contraction

et al. 2006

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“…This investigation is helped by the technological advances in computers, along with advanced signal processing and machine learning methods. Therefore, many researches have proposed automated methods and systems capable of analyzing the FHR (Arduini, et al, 1993;Berdinas, et al, 2002;Bernardes, et al, 1991;Magenes, et al, 2000;Cazares, et al, 2001;Chung, et al, 1995;Dawes, et al, 1995;Jezewski, and Wrobel, 1993;Krause, 1990;Maeda, et al, 1990;Mantel, et al, 1990a;Mantel, et al, 1990b;Salamelekis, et al, 2002;Skinner, et al, 1999;Taylor, et al, 2000). Most of the research efforts aimed to propose methodologies, not only to record, store and display the FHR, but also to classify FHR and produce indices alerting when the fetus is on the verge of severe compromise (metabolic acidosis that may lead to cerebral palsy or even death).…”

Section: Introductionmentioning

confidence: 99%

Classification of Fetal Heart Rate Using Scale Dependent Features and Support Vector Machines

Georgoulas

Stylios

Groumpos

2005

IFAC Proceedings Volumes

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One new approach for the problem of feature extraction and classification of Fetal Heart Rate signal is introduced in this paper. It considers the use of the Discrete Wavelet Transformation to extract scale-dependent features of Fetal Heart Rate (FHR) signal and the use of Support Vector Machines for classification of FHR. The proposed methodology is tested on real data acquired just before delivery. The results proved the viability of the approach and its potential for further application by achieving an overall classification performance of 90%.

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“…• Morphological -large and small accelerations per hour, decelerations per hour and contractions per hour • Time domain -FHR mean over a minute (mean FHR), FHR standard deviation (std FHR) , Delta FHR, Short term variability (STV), Long term irregularity (LTI), Interval Index (II), • Frequency domain from autoregressive power spectrum estimation -LF-power, MF-power, HFpower and LF/(MF+HF)) and • Regularity parameters -approximate entropy (ApEn) [7] Accelerations and decelerations were computed automatically by the Mantel's algorithm [3], as well as the uterine contractions, that resulted from the application of a modified version of the FHR baseline computation applied to the tocographic signal. The remaining parameters were computed as reported in [8].…”

Section: Parameters Extractionmentioning

confidence: 99%

“…The obvious consequence, in a false positive case, can be to decide to execute a caesarean when it is not necessary, or , in a false negative case to let the pregnancy go on, when a caesarean should have been done. Although a number methods for judging CTG recordings were proposed [2,3,4] and few systems able of automatically computing quantitative parameters were developed [5,6], no one of them showed a strong reliability in predicting the fetal well-being The aim of the present work is to test and compare several classification techniques of the CTG signal, allowing to evolve into a reliable automatic system of "reading" and analysing the CTG tracings in the hope of diagnosing any eventual fetal suffering status. …”

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confidence: 99%

Automatic diagnosis of fetal heart rate: comparison of different methodological approaches

Magenes

Signorini²,

Sassi³

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

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Abstract-The cardiotocograpy (CTG) is the clinical, traditional, noninvasive approach to monitor the fetal condition antepartum. CTG analysis is focused on the detection of fetal heart rate parameters from which the clinicians can identify by eye inspection some patterns associated to fetal activity. However this qualitative method rarely can detect the emergence of fetal pathologies. This study aims at finding new algorithms which can enhance the differences among the normal CTG signals and those presenting anomalies due to a pathological status. On a database of more than 500 recordings, we tested different classification methods to identify normals from potential pathological fetuses. A Multilayer Perceptron (MLP) neural network and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were compared with classical statistical methods. Both the neural and neuro -fuzzy approaches seem to give better results than any tested statistical classifier. Keywords -Fetal Heart Rate, Neural Networks, Fuzzy Systems, Multivariate Methods I. INTRODUCTIONThe cardiotocography (CTG) is regularly monitored in the clinical routine antepartum and during the labour in order to prevent a possible fetal sufferance status. It consists of the simultaneous recording and printout of two signals: the heartbeat frequency of the fetus and the toco signal, relative to the uterine contractions. It was only at the end of the 60's, when the fetal heartbeat could be rather easily detected by means of ultrasound (the Doppler-shift) or through the application of direct electrocardiography, that cardiotocography became popular as the method to monitor the condition of the fetus. This modality provides not only continuous heart rate information, but also fetal heart rate changes in response to uterine contractions. Currently the majority of obstetric decisions to assist delivery of the baby by artificial means (caesarean section, forceps or vacuum extraction) for reasons of suspected fetal distress, relies on information gathered through the application of cardiotocography. It is the obstetrician's reassurance that if the fetal heart rate (FHR) pattern is normal then there is the nearly 100% certainty that the fetus is in a good condition, which has made cardiotocography so attractive and has induced its widespread use [1]. The classical cardiotocographic analysis by simple eye inspection has drastically reduced the incidence of deaths during the labour and also in premature newborns, although the presence of many false positives. This problem can be attributed to the inter and intra-observer variability, in the interpretation of the CTG signals, due in the first case to the different levels of experience of the various specialists , and in the second to suggestive factors (stress, environmental conditions). The reading of the printout in most cases is mainly a subjective process and, in certain cases, may lead to a wrong decision. The obvious consequence, in a false positive case, can be to decide to execute a caesarean when it is not necessary, or , i...

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Computer analysis of antepartum fetal heart rate: 1. Baseline determination

Cited by 102 publications

References 10 publications

Foetal heart rate power spectrum response to uterine contraction

Foetal heart rate power spectrum response to uterine contraction

Classification of Fetal Heart Rate Using Scale Dependent Features and Support Vector Machines

Automatic diagnosis of fetal heart rate: comparison of different methodological approaches

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