This paper presents some aspects of the on-line remote fetal monitoring system based on the GPRS data transmission service and WAN network. Th e signal recording instrumentation consists of standard fetal monitor and PDA computer. Th e central surveillance system located in hospital has a capability of simultaneous monitoring of up to 24 patients, both remotely and within hospital. Th e system enables analysis, dynamic presentation and archiving of signals and medical data. Th e clinical interpretation is immediately available in hospital as well as at patient’s home or attending doctor offi ce.

• MeSH
• automatizované zpracování dat metody   využití   MeSH
• financování organizované MeSH
• internet využití   MeSH
• komplikace těhotenství diagnóza   prevence a kontrola   MeSH
• lidé MeSH
• monitorování plodu metody   přístrojové vybavení   využití   MeSH
• počítače do ruky využití   zásobování a distribuce   MeSH
• počítačové komunikační sítě využití   MeSH
• telekomunikace využití   MeSH
• telemedicína metody   přístrojové vybavení   využití   MeSH
• Check Tag
• lidé MeSH

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 diffi cult than in electrocardiography, where the R–waves are evident. We investigated the infl uence of the autocorrelation window size on the FHR variability analysis. The indices describing the FHR variability calculated for signals obtained with two autocorrelation algorithms for 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.

• MeSH
• algoritmy MeSH
• automatizované zpracování dat metody   využití   MeSH
• elektrokardiografie metody   přístrojové vybavení   využití   MeSH
• financování organizované MeSH
• lidé MeSH
• monitorování plodu metody   přístrojové vybavení   využití   MeSH
• srdeční frekvence plodu fyziologie   MeSH
• statistika jako téma metody   MeSH
• ultrasonografie metody   přístrojové vybavení   využití   MeSH
• Check Tag
• lidé MeSH

The most commonly used method of fetal monitoring is based on heart activity analysis. Computer-aided fetal monitoring system enables extraction of clinically important information hidden for visual interpretation-the instantaneous fetal heart rate (FHR) variability. Today's fetal monitors are based on monitoring of mechanical activity of the fetal heart by means of Doppler ultrasound technique. The FHR is determined using autocorrelation methods, and thus it has a form of evenly spaced-every 250 ms-instantaneous measurements, where some of which are incorrect or duplicate. The parameters describing a beat-to-beat FHR variability calculated from such a signal show significant errors. The aim of our research was to develop new analysis methods that will both improve an accuracy of the FHR determination and provide FHR representation as time series of events. The study was carried out on simultaneously recorded (during labor) Doppler ultrasound signal and the reference direct fetal electrocardiogram Two subranges of Doppler bandwidths were separated to describe heart wall movements and valve motions. After reduction of signal complexity by determining the Doppler ultrasound envelope, the signal was analyzed to determine the FHR. The autocorrelation method supported by a trapezoidal prediction function was used. In the final stage, two different methods were developed to provide signal representation as time series of events: the first using correction of duplicate measurements and the second based on segmentation of instantaneous periodicity measurements. Thus, it ensured the mean heart interval measurement error of only 1.35 ms. In a case of beat-to-beat variability assessment the errors ranged from -1.9% to -10.1%. Comparing the obtained values to other published results clearly confirms that the new methods provides a higher accuracy of an interval measurement and a better reliability of the FHR variability estimation.

• MeSH
• analýza dat MeSH
• elektrokardiografie MeSH
• lidé MeSH
• monitorování plodu * MeSH
• reprodukovatelnost výsledků MeSH
• srdeční frekvence plodu * MeSH
• srdeční frekvence MeSH
• těhotenství MeSH
• ultrasonografie dopplerovská MeSH
• Check Tag
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19-93.88%], sensitivity 95.09% [95% confidence interval: 93.68-96.03%], positive predictive value 96.36% [95% confidence interval: 95.05-97.17%] and F1-score 95.69% [95% confidence interval: 94.83-96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44-81.85%], sensitivity 81.79% [95% confidence interval: 76.59-85.43%], positive predictive value 87.16% [95% confidence interval: 81.95-90.35%] and F1-score 84.08% [95% confidence interval: 80.75-86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values μ < 0.1 and values of ±1.96σ < 0.1).

• MeSH
• algoritmy * MeSH
• břicho fyziologie   MeSH
• databáze faktografické MeSH
• elektrokardiografie metody   MeSH
• lidé MeSH
• matky statistika a číselné údaje   MeSH
• monitorování plodu metody   MeSH
• plod fyziologie   MeSH
• počítačové zpracování signálu přístrojové vybavení   MeSH
• srdeční frekvence plodu fyziologie   MeSH
• těhotenství MeSH
• Check Tag
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

• MeSH
• děložní kontrakce fyziologie   MeSH
• finanční podpora výzkumu jako téma MeSH
• lidé MeSH
• měniče využití   MeSH
• těhotenství MeSH
• Check Tag
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

• MeSH
• algoritmy MeSH
• databáze faktografické MeSH
• diagnóza počítačová MeSH
• elektrokardiografie metody   MeSH
• fibrilace síní diagnóza   patofyziologie   MeSH
• lidé MeSH
• počítačové zpracování signálu MeSH
• srdeční frekvence fyziologie   MeSH
• support vector machine MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• finanční podpora výzkumu jako téma MeSH
• intenzivní péče o novorozence metody   MeSH
• lidé MeSH
• monitorování fyziologických funkcí metody   přístrojové vybavení   MeSH
• oxymetrie metody   přístrojové vybavení   MeSH
• transkutánní měření krevních plynů metody   přístrojové vybavení   MeSH
• Check Tag
• lidé MeSH

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.

• Publikační typ
• časopisecké články MeSH

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.

• MeSH
• algoritmy MeSH
• elektrody MeSH
• elektrokardiografie MeSH
• lidé MeSH
• monitorování plodu * MeSH
• počítačové zpracování signálu MeSH
• těhotenství MeSH
• Check Tag
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH