Pregnancy Telemonitoring with Smart Control of Algorithms for Signal Analysis

Wróbel, Janusz; Matonia, A.; Horoba, K.; Jeżewski, Janusz; Czabański, Robert; Pawlak, Adam; Porwik, Piotr

doi:10.1166/jmihi.2015.1533

Cited by 22 publications

(7 citation statements)

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“…Therefore, the best quality FECG signal selected with dedicated measures was used to detect fetal QRS complexes. In order to determine the signal quality index based on the quasi-periodicity of the signal, the autocorrelation function was used, calculated in windows of width being adjusted to the fetal heart rate 41 . Automated pre-detection and manual correction of fetal R-wave positions were applied after selecting the best quality FECG signal.…”

Section: Pregnancy (Antenatal) Signalsmentioning

confidence: 99%

“…Our research team has made a basic assumption to focus only on methods that do not require additional chest electrodes. The operation of online algorithms, as well as their practical implementation in a small, mobile device for telemedical applications, was also considered to be a very important requirement 5,41 . In the works 35 we proposed a maternal electrocardiogram suppression method that was based on subtracting the maternal signal template covering the full heart cycle -a PQRST complex.…”

Section: Technical Validationmentioning

confidence: 99%

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Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

et al. 2020

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Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. the resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.

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Section: Pregnancy (Antenatal) Signalsmentioning

confidence: 99%

Section: Technical Validationmentioning

confidence: 99%

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

et al. 2020

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“…In this paper, we describe the method for automated AF detection which assigns the vector of parameters quantitatively describing the HR signal into two classes representing the absence or presence of atrial fibrillation. As estimation of HR variability is also important part of the Fetal Heart Rate (FHR) analysis [47][48][49], the indices widely used for FHR variability description have been considered as potentially useful for AF detection. The detection method presented in this paper was derived from the machine learning principles.…”

Section: Introductionmentioning

confidence: 99%

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Czabański

Horoba

Wróbel

et al. 2020

Sensors

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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%.

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“…To date, the possibilities of parameterization and objectification of the FHR variability evaluation relate to accurate quantitative signal analysis carried out by dedicated computer-aided fetal monitoring systems [46][47][48][49][50][51]. Automated signal analysis ensures repeatability of interpretation [52][53][54][55][56], as well as the extraction of information hidden for visual assessment, such as instantaneous FHR variability at a beat-to-beat level [57]. At turn of the 60 s and 70 s, several different indices were defined for quantitative description of the instantaneous FHR variability [58].…”

Section: Introductionmentioning

confidence: 99%

New Method for Beat-to-Beat Fetal Heart Rate Measurement Using Doppler Ultrasound Signal

Kupka

Matonia

Jeżewski

et al. 2020

Sensors

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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.

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Pregnancy Telemonitoring with Smart Control of Algorithms for Signal Analysis

Cited by 22 publications

References 0 publications

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

New Method for Beat-to-Beat Fetal Heart Rate Measurement Using Doppler Ultrasound Signal

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