Computer analysis of antepartum fetal heart rate: 2. Detection of accelerations and decelerations

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

doi:10.1016/0020-7101(90)90031-o

Cited by 69 publications

(26 citation statements)

References 8 publications

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“…baseline and floating line-running mean and median line-of the FHR signal). To filter out these components from the raw FHR signals, the baseline and the floating line [29,30] were computed (by a smoothing cubic spline) and then subtracted from the FHR signal [41]. Actually, this operation corresponds to a high-pass filtering (about 0.03 Hz) of the signal.…”

Section: And Vlf Suppressionmentioning

confidence: 99%

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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: And Vlf Suppressionmentioning

confidence: 99%

“…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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“…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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“…Parameters might be grouped as: -Morphologic -large and small accelerations per hour [7]; -  Time domain -FHR mean over a minute (mean FHR), Delta, Short term variability (STV), Long term irregularity (LTI), Interval Index (II) [8]; -Frequency domain from autoregressive power spectrum estimation -LF-power, MF-power, HF-power and LF/(MF+HF)) [9]. - Regularity parameters -Approximate Entropy (ApEn) [10].…”

Section: Parametersmentioning

confidence: 99%