Fetal heart rate variability: clinical experts versus computerized system interpretation

Jeżewski, Janusz; Wróbel, Janusz; Horoba, K.; Gacek, Adam; Sikora, Jerzy

doi:10.1109/iembs.2002.1106566

Cited by 19 publications

(15 citation statements)

References 1 publication

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“…Generated signals were used to compare the baselines that have been estimated by two algorithm: the first one relying on artificial neural networks and the classical one using nonlinear filtering of FHR signal [1], [6]. The neural network of multilayer perceptron type with sigmoidal function was chosen, that had 120-10-1 structure [11].…”

Section: Resultsmentioning

confidence: 99%

“…This corresponds to 14.400 samples of 4 Hz rate, which is a digital form of FHR signal in computer-aided systems. The baseline component was modeled by means of an algorithm based on nonlinear bidirectional low-pass filtering in order to ensure linear phase characteristic [1], [6]. The filter is given by the following equation:…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the interpretation can change under the influence of tiredness or level of clinical experience of a person analyzing the trace. Hence, the computer-aided monitoring systems have been widely applied to improve objectivity and repeatability of signal interpretation [1].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Fetal Heart Rate Baseline Estimation Method Using Testing Signals Based on a Statistical Model

Kupka

Wróbel

Jeżewski

et al. 2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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Computer-aided fetal monitoring is based on automated analysis of the fetal heart rate (FHR) variability. The first and the main step in the automated signal interpretation is the estimation of the so called FHR baseline. There are various algorithms for baseline estimation, of different efficiency. For its evaluation, the method of modeling of FHR signal based on the preset baseline component has been developed. The best algorithm is expected to provide the same baseline as the component baseline used to model the FHR signal. Generated signals were used to compare the baselines that have been estimated by two algorithm: the first one relying on artificial neural networks and the classical one using nonlinear filtering of FHR signal.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Fetal Heart Rate Baseline Estimation Method Using Testing Signals Based on a Statistical Model

Kupka

Wróbel

Jeżewski

et al. 2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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“…One of the reasons for the production of FP results is the problematic interpretation of CTG, which suffers from large inter-observer disagreement (see Bernardes et al, 1997 ; Blix et al, 2003 ; Vayssière et al, 2010 ; Blackwell et al, 2011 ; Hruban et al, 2015 ). This is clearly noticeable when we compare the clinical expert interpretation of the fetal Heart Rate Variability (fHRV) with those generated by computerized systems (Jezewski et al, 2002 ). The accuracy of the CTG method can be increased by using an internal probe that measures the fetal heart rate directly from the fetal scalp (Amer-Wåhlin et al, 2001 ; Jezewski et al, 2012 ).…”

Section: State Of the Artmentioning

confidence: 96%

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring

et al. 2018

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Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01–real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direct Fetal Electrocardiogram Database), which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data (i.e., around 88,000 R waves) for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work.

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“…However, for the purpose of automated analysis of the FHR signal (i.e., determination of FHR baseline, detection of acceleration or deceleration events, and quantification of other important clinical features describing the FHR signal) the missing values have to be replaced by valid ones. 14 Most often they are replaced by a mean value of the fetal heart rate estimated from the neighboring measurements or using a linear interpolation of them.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Robustness of Fetal Heart Rate Variability Measures to Low Signal Quality

Wróbel¹,

Roj²,

Jeżewski³

et al. 2015

J Med Imaging Hlth Inform

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The most important features indicating appropriate fetal development are the measures of instantaneous variability of a fetal heart rate, describing fluctuations of the beat-to-beat heart intervals. The most popular method for the fetal heart rate acquisition is the Doppler ultrasound technique. However, it is very sensitive to various motion artifacts distorting the signal being acquired. The aim of our work was to evaluate the influence of signal loss episodes on the parameters quantitatively describing the instantaneous variability of the fetal heart rate. For this purpose we artificially inserted signal loss episodes to the cardiotocographic recordings from both antepartum and intrapartum databases. The episodes were applied in different patterns and percentage, in accordance with the real characteristics of the signal loss segments. It was proven that the signal loss episodes may cause significant reduction of variability indices, in some cases to the degree potentially affecting the clinical evaluation of a signal. The sensitivity of particular indices was directly compared and the measures most robust to missing values were revealed.

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Fetal heart rate variability: clinical experts versus computerized system interpretation

Cited by 19 publications

References 1 publication

Evaluation of Fetal Heart Rate Baseline Estimation Method Using Testing Signals Based on a Statistical Model

Evaluation of Fetal Heart Rate Baseline Estimation Method Using Testing Signals Based on a Statistical Model

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring

Evaluation of the Robustness of Fetal Heart Rate Variability Measures to Low Signal Quality

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