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1

Coping with limitations of fetal monitoring instrumentation to improve heart rhythm variability assessment

Kupka Tomasz, Matonia Adam, Jezewski Michal, Horoba Krzysztof, Wrobel Janusz, Jezewski Janusz

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

388--403

EN

The most commonly used method of fetal monitoring is based on analysis of the fetal heart activity. Computer-aided fetal monitoring enables extraction of information hidden for visual interpretation – the instantaneous fetal heart rate (FHR) variability. The most natural method of obtaining FHR signal is fetal electrocardiography (FECG), where the FHR has a natural form of unevenly spaced time series of events – heart beats detected in FECG. However, because of problems with FECG recording, the today's instrumentation is based on monitoring of mechanical activity of the fetal heart by means of Doppler ultrasound technique. The ultrasound signal periodicity is determined with autocorrelation methods, so the FHR output signal has a form of evenly spaced instantaneous measurements, some of which are incorrect or duplicate. The aim of the work was to develop a correction algorithm for recognition and removal of these invalid values, to reproduce the FHR signal as time series of events. The new algorithm was compared to other known methods basing on the collected research material and defined performance measures. Thanks to the reference FECG signal registered simultaneously, a detailed analysis of algorithms performance at the level of true heart cycles was possible. Additionally, the influence of signal correction on indices describing the instantaneous FHR variability was evaluated. The obtained results showed that although changing the FHR signal form into time series of events improved the accuracy of indices, but in relation to beat-to-beat variability, that accuracy still does not ensure reliable analysis of instantaneous FHR variability.

2

Evaluating the fetal heart rate baseline estimation algorithms by their influence on detection of clinically important patterns

Jezewski J., Horoba K., Roj D., Wrobel J., Kupka T., Matonia A.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

562--573

EN

A correctly estimated component of fetal heart rate signal (FHR) – so called baseline – is a precondition for proper recognition of acceleration and deceleration patterns. A number of various algorithms for estimating the FHR baseline was proposed so far. However, there is no reference standard enabling their objective evaluation, and thus no methodology of comparing the different algorithms still exists. In this paper we propose a method for evaluation of automatically determined baseline in reference to a set of experts, based on ten separate groups of signals comprising typical variability patterns observed in the fetal heart rate. As it was proposed earlier [1], the given algorithm is evaluated based on the characteristic patterns detected using the obtained baseline, instead of direct analysis of the baseline shape. For the purpose of quantitative assessment of the estimated baseline a new synthetic inconsistency coefficient was applied. The proposed methodology enabled to evaluate eleven well-known algorithms. We believe that the method will be a valuable tool for assessment of the existing algorithms, as well as for developing the new ones.

3

Automated detection of uterine contractions in tocography signals – Comparison of algorithms

Horoba K., Wrobel J., Jezewski J., Kupka T., Roj D., Jezewski M.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

610--618

EN

Monitoring of uterine contractile activity enables to control the progress of labor. Automated detection of contractions is an integral part of the signal analysis implemented in computer- aided fetal surveillance system. Comparison of four algorithms for automated detection of uterine contractions in the signal of uterine mechanical activity is presented. Three algorithms are based generally on analysis of the frequency distribution of signal values. The fourth method relies on analyzing the rate of changes of the uterine activity signal. The reference data in form of beginning and end of contraction episodes were provided by human experts. Obtained results show that all algorithms were capable to detect above 91% reference contractions, and less than 7% of recognized patterns were false. Two algorithms can be distinguished as providing a higher performance expressed by the sensitivity of 95% and the positive predictive value of 97%. Such results could be obtained by optimization of contraction validation criteria.

4

Improving the efficacy of automated fetal state assessment with fuzzy analysis of delivery outcome

Czabanski R., Jezewski M., Horoba K., Jezewski J., Leski J.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

223--230

EN

A number of methods of the qualitative assessment of fetal heart rate (FHR) signals are based on supervised learning. The classification methods based on the supervised learning require a set of training recordings accompanied by the reference interpretation. In the real data collections the class of signals related to fetal distress is usually under-represented. Too small percentage of distress patterns adversely affects the effectiveness of the automated evaluation of the fetal state. The paper presents a method of equalizing the class sizes based on the reference assessment of the fetal state with the fuzzy analysis of the newborn attributes. The supervised learning with increased number of the FHR signals, which are characterized by the highest rate of the fuzzy inference leads to significant increase of the efficacy of the qualitative assessment of the fetal state using the Lagrangian support vector machine.

5

A novel approach to comparison of the fetal heart rate baseline estimation algorithms

Jezewski J., Horoba K., Roj D., Wrobel J., Kupka T., Matonia A.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

231--238

EN

A number of algorithms for estimating the so called fetal heart rate baseline was proposed so far. However, there is no reference pattern enabling their objective evaluation, and thus no methodology of comparing the competing algorithms still exists. In this paper we propose a method for evaluation of automatically determined baseline in reference to a group of experts, basing on ten separate groups of signals comprising typical patterns observed in the fetal heart rate. For the purpose of quantitative assessment of the estimated baseline a new synthetic inconsistency coefficient is presented. The proposed methodology was applied to evaluate ten well-known algorithms. We believe that the method will be a valuable tool for assessment of the existing algorithms, as well as for developing new ones.

6

Smart selection of signal analysis algorithms for telecare of high-risk pregnancy

Wróbel J., Matonia A., Horoba K., Jeżewski J., Czabański R., Pawlak A., Porwik P.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

27--33

EN

Telemedical system for fetal home monitoring with smart selection of signal analysis algorithms is presented in this paper. Fetal monitoring signals are provided by a mobile instrumentation consisting of bioelectrical signal recorder and tablet PC which retrieves and processes the data as well as provides wireless data transmission based on Internet. The fetal surveillance system enables analysis, dynamic presentation and archiving of acquired signals and medical data. Novelty of the proposed approach relies on smart fitting of the algorithms for analysis of the abdominal signals in mobile instrumentation, as well as on controlling of the fetal monitoring session from the surveillance center. These actions are performed automatically through continuous analyzing of the signal quality and the reliability of the quantitative parameters determined for the acquired signals. Using that approach the amount and content of data transmitted through remote channels to the surveillance center can be controlled to ensure the most reliable assessment of the fetal well-being.

7

Improving the quality of the fetal state assessment with epsilon-insensitive learning methods

Czabański R., Wróbel J., Jeżewski J., Łęski J.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

19--26

EN

Recording and analysis of fetal heart rate (FHR) signal is nowadays the primary method for the biophysical assessment of the fetal state. Since the correct interpretation of crucial FHR characteristics is difficult, methods of automated quantitative signal evaluation are still the subject of the research studies. In the following paper we investigated the possibility of improvement of the fetal state evaluation on the basis of the epsilon-insensitive learning (eIL). We examined two eIL procedures integrated with fuzzy clustering algorithms as well as different methods of logical interpretation of the fuzzy conditional statements. The quality of the FHR signal classification was evaluated using the data collected with the computerized fetal surveillance system. The learning performance was measured with the number of correct classification (CC) and overall quality index (QI) defined as a geometric mean of sensitivity and specificity. The obtained results (CC = 88 % and QI = 87 %) show a high efficiency of the fetal state assessment using the epsilon-insensitive learning based methods.

8

Fetal state evaluation with fuzzy analysis of newborn attributes using CUDA architecture

Czabański R., Wróbel J., Jeżewski J.

Journal of Medical Informatics & Technologies

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2013

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Vol. 22

125--133

EN

Cardiotocography is a biophysical method of fetal state evaluation involving the recording and analysis of the fetal heart rate (FHR). Since a proper interpretation of the signal is relatively difficult, an automatic classification is often based on computational intelligence methods. The quality of classifiers based on supervised learning algorithms depends on a proper selection of learning data. In case of the fetal state evaluation, the learning is usually based on a set of quantitative parameters of FHR signal and the corresponding reference information determined on the basis of the retrospective analysis of newborn attributes. Values of the single attribute have been used so far as a reference. As a result, a part of information on the actual neonatal outcome has always been lost. The following paper presents a method of the fuzzy reasoning leading to an evaluation of neonatal outcome as a function of three newborn attributes. The fuzzy system was used in the process of a qualitative evaluation of the fetal state based on quantitative analysis of FHR signal using a support vector machine (SVM). In order to improve computational effectiveness, the learning algorithm was implemented in Compute Unified Device Architecture (CUDA). The results of these studies confirm the effectiveness of the proposed method and indicate the possibility of practical usage of the fuzzy system in supervised learning algorithms for the qualitative evaluation of the fetal state.

9

Fetal state assessment using fuzzy analysis of fetal heart rate signals - Agreement with the neonatal outcome

Czabański R., Jeżewski J., Horoba K., Jeżewski M.

Biocybernetics and Biomedical Engineering

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2013

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Vol. 33, no. 3

145--155

EN

Fetal monitoring is based on analysis of fetal heart rate signal. Visual interpretation is difficult so computer-aided systems for quantitative analysis are commonly used. The clinical interpretation guidelines provided by FIGO (Fédération Internationale de Gynécologie et d'Obstétrique) were used to develop the weighted fuzzy scoring system for qualitative assessment of the fetal state. In this work, agreement of the fuzzy classification system with the neonatal outcome assessment was analyzed. Various datasets were evaluated, depending on interpretation method of the signals which were recorded from patients. The obtained results confirmed possibility of the efficient fetal state assessment using the fuzzy inference method proposed.

10

Comparison of instantaneous fetal heart rate extracted from abdominal and direct fetal electrocardiograms

Matonia A., Kupka T., Jezewski J., Momot A., Jeżewski M., Bernys M.

Journal of Medical Informatics & Technologies

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2012

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Vol. 19

101--107

EN

This work is an attempt to assess the reliability of indirect abdominal electrocardiography as an alternative technique of fetal monitoring. As a reference signal we used the simultaneously acquired direct fetal electrocardiogram. Each recording consisted of four signals acquired from maternal abdomen and the reference signal acquired directly from fetal head. The first stage of our study concerned the signal loss episodes. In order to reduce the influence of incorrectly detected R-waves, some certain validation rules were applied. In the second stage, the corresponding intervals determined on basis of both acquisition methods were matched and the accuracy of fetal heart rate measurement was evaluated. Although the accuracy of abdominal electrocardiography turned out to be slightly lower than reported for ultrasound method, it still has some unique features deciding of its prevalence in a certain circumstances.

11

A routing protocol for wireless network of bedside monitors in fetal surveillance system

Seredynski R., Horoba K., Roj D., Bernys M., Przybyla T., Jezewski J.

Journal of Medical Informatics & Technologies

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2012

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Vol. 21

27--33

EN

The paper presents the methodology of wireless network design, developed according to the requirements originating from existing wired fetal surveillance systems. The proposed network structure is based on popular radio frequency modules, operating in 433/866MHz band. The described solution is a simple and cost effective alternative to the wired networks, and it will vastly increase the mobility of fetal monitors. The authors also describe software tools which were designed for this purpose and the results of simulations performed on their basis.

12

Fuzzy prediction of fetal acidemia

Czabański R., Roj D., Jeżewski J., Horoba K., Jeżewski M.

Journal of Medical Informatics & Technologies

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2011

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Vol. 17

81--87

EN

Cardiotocography is the primary method for biophysical assessment of a fetal state. It is based mainly on the recording and analysis of fetal heart rate signal (FHR). Computer systems for fetal monitoring provide a quantitative description of FHR signals, however the effective methods for their qualitative assessment are still needed. The measurements of hydronium ions concentration (pH) in newborn cord blood is considered as the objective indicator of the fetal state. Improper pH level is a symptom of acidemia being the result of fetal hypoxia. The paper proposes a twostep analysis of signals allowing for effective prediction of the acidemia risk. The first step consists in the fuzzy classification of FHR signals. The task of fuzzy inference is to indicate signals that according to the FIGO guidelines represent the fetal wellbeing. These recordings are eliminated from the further classification with Lagrangian Support Vector Machines. The proposed procedure was evaluated using data collected with computerized fetal surveillance system. The classification results confirmed the high quality of the proposed fuzzy method of fetal state evaluation.

13

Improving the periodicity measurement in fetal heart activity signal

Roj D., Wróbel J., Horoba K., Przybyła T., Kupka T.

Journal of Medical Informatics & Technologies

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2010

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Vol. 16

19--26

EN

In this paper we discussed the influence of preliminary processing of the ultrasound Doppler signal on accuracy of the fetal heart rate estimation as well as on reliability of the FHR instantaneous variability assessment. We attempted to develop an optimal processing channel of US Doppler signal in order to measure the periodicity of fetal heart activity with accuracy as close as possible to that ensured by FECG. The FHR values determined from the US signal were compared to the reference data obtained from direct FECG. In a final evaluation we used the parameters describing the FHR variability as the clinically important signal features being the most sensitive to any periodicity inaccuracy. The results proved that an application of proposed algorithms improves the accuracy of interval measurements and FHR instantaneous variability assessment in relation to the new-generation fetal monitors.

14

Evaluation of predictive capabilities of quantitative cardiotocographic signal features

Czabański R., Jeżewski M., Roj D., Szaszkowski Z., Kupka T., Wróbel J.

Journal of Medical Informatics & Technologies

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2010

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Vol. 16

11--17

EN

Cardiotocography (CTG) is the main method of assessment of the fetal state during pregnancy and labour used in clinical practice. It is based on quantitative analysis of fetal heart rate, fetal movements and uterine contractions signals. The evaluation of the CTG signals can be made using criteria recommended by International Federation of Obstetrics and Gynecology. Nevertheless, the diagnosis verification is possible only after the delivery on the basis of newborn assessment. In the proposed work we evaluated the capacity of quantitative analysis of CTG traces in predicting fetal outcome. The relationship between CTG signal features and attributes of fetal outcome was assessed on the basis of ROC curves analysis. The obtained results indicate the adequate predictive capabilities of the selected CTG features especially for fetal outcome assessed with Apgar score and suggest the necessity of applying the criteria for the CTG traces evaluation that are related to the gestational age. Our study also shows the value of the CTG monitoring as a screening procedure providing appropriate confirmation of fetal wellbeing.

15

Unsupervised clustering for fetal state assessment based on selected features of the cardiotocographic signals

Przybyła T., Jeżewski J., Roj D.

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

157--162

EN

In modern obstetrics the cardiotocography is a routine method of fetal condition assessment based mainly on analysis of the fetal heart rate signals. The correct interpretation of recorded traces from a bedside monitor is very difficult even for experienced clinicians. Therefore, computerized fetal monitoring systems are used to yield the quantitative description of the signal. However, the effective techniques enabling automated conclusion generation based on cardiotocograms are still being searched. The paper presents an attempt to diagnose the fetal state basing on seventeen features describing the cardiotocographic records. The proposed method applies the unsupervised classification of signals. During our research we tried to classify the fetal state using the fuzzy c-means (FCM) clustering. We also tested how the efficiency of classification could be influenced by application of principal component analysis (PCA) algorithm. The obtained results showed that unsupervised classification cannot be considered as a support to fetal state assessment.

16

Fuzzy system for evaluation of fetal heart rate signals using FIGO criteria

Czabański R., Jeżewski M., Wróbel J., Jeżewski J., Horoba K.

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

189--194

EN

Cardiotocography is a biophysical method of fetal monitoring during pregnancy and labour. It is mainly based on recording and analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the recorded signals but the effective methods supporting the conclusion generation are still needed. The evaluation of the signal can be made using criteria recommended by FIGO. Nevertheless, the quantitative description of the traces is inconsistent with qualitative nature of the obstetric knowledge. Therefore, we applied the fuzzy system based on Takagi-Sugeno-Kang model to evaluate and classify signals. FIGO guidelines were used for developing a set of fuzzy conditional rules defining the system performance. The proposed system was evaluated using data collected with computerized fetal surveillance system – MONAKO. The classification results confirm the improvement of the fetal state evaluation quality while using the proposed fuzzy system support.

17

Wybrane aspekty rejestracji mechanicznej aktywności serca płodu, a dokładność pomiaru zmienności rytmu

Roj D., Wróbel J., Jeżewski J., Kotas M., Przybyła T.

Aktualne Problemy Biomechaniki

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2009

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z. 3

201--206

PL

Podstawową metodą rejestracji rytmu pracy serca płodu jest monitorowanie mechanicznej czynności serca, na podstawie analizy efektu przesunięcia dopplerowskiego wiązki ultradźwiękowej, odbitej od poruszających się zastawek. Złożony kształt sygnału, obejmującego liczne zdarzenia ruchowe w ramach jednego cyklu serca, sprawia, że pomiar okresowości może być obarczony błędem. W pracy omówiono wpływ położenia przetwornika ultradźwiękowego na dokładność pomiaru rytmu serca oraz możliwość poprawy precyzji przez analizę sygnału dopplerowskiego w ograniczonych pasmach.

EN

Commonly used method of fetal heart rate acquisition is based on monitoring of mechanical activity of fetal heart, using the Doppler effect in ultrasound wave reflected from the heart valves. The complex form of signal, containing several valve movements in a single heart cycle, results in high value of interval measurement error. We investigated the influence of transducer placement on precision of fetal heart rate determination and the possibility of accuracy improvement by limitation of signal bandwidth.

18

The influence of window size of autocorrelation function on fetal heart rate variability measurement using the Doppler ultrasound signal

Roj D., Fuchs T., Przybyła T., Jeżewski M., Matonia A., Gacek A.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

111--116

EN

Commonly used noninvasive fetal monitoring is based on fetal heart rate (FHR) variability analysis of the Doppler ultrasound signal coming from the mechanical activity of the fetal heart. Estimation of periodicity of acquired signals using the autocorrelation technique is very important. The determination of cardiac intervals using the Doppler signal is more difficult than in electrocardiography, where the R-waves are evident. We investigated the influence of the autocorrelation window size on the FHR variability analysis. The indices describing the FHR variability calculated for signals obtained using two different autocorrelation techniques with various window lengths were compared with the reference ones obtained from fetal electrocardiogram. The optimal window was a compromise between artifacts resistance and the averaging level of instantaneous variability.

19

Separation of abdominal fetal electrocardiograms in twin pregnancy

Kotas M., Jeżewski J., Matonia A., Kupka T.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

83--89

EN

A combined application of independent component analysis and projective filtering of the time-aligned ECG beats is proposed to solve the problem of fetal ECG extraction from multi-channel maternal abdominal electric signals. The developed method is employed to process the four-channel abdominal signals recorded during twin pregnancy. The signals are complicated mixtures of the maternal ECG, the ECGs of the fetal twins and noise of other origin. The independent component analysis cannot separate the respective signals, but the proposed combination of the methods allows to suppress the maternal ECG and when the level of noise is low it leads to an effective separation of the twins' signals.

20

Influence of gestational age on neural networks interpretation of fetal monitoring signals

Jeżewski M., Czabański R., Horoba K., Wróbel J., Łęski J., Jeżewski J.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

137--142

EN

Cardiotocographic monitoring (CTG) is a primary biophysical monitoring method for assessment of the fetal state and is based on analysis of fetal heart rate, uterine contraction activity and fetal movement signals. Visual analysis of CTG traces is very difficult so computer-aided fetal monitoring systems have become a standard in clinical centres. We proposed the application of neural networks for the prediction of fetal outcome using the parameters of quantitative description of acquired signals as inputs. We focused on the influence of the gestational age (during trace recording) on the fetal outcome classification quality. We designed MLP and RBF neural networks with changing the number of neurons in the hidden layer to find the best structure. Networks were trained and tested fifty times, with random cases assignment to training, validating and testing subset. We obtained the value of sensitivity index above 0.7, what may be regarded as good result. However additional trace grouping within similar gestational age, increased classification quality in the case of MLP networks.