Enhancement of low-quality fetal electrocardiogram based on time-sequenced adaptive filtering

Fotiadou, E.; Laar, Judith O E H van; Oei, S.G.; Vullings, Rik

doi:10.1007/s11517-018-1862-8

Cited by 29 publications

(22 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the abdominal signals, the amplitude of the fetal QRS complex (FQRS) hardly ever exceeds 20 μV and strongly depends on the maternal body mass index 14,15 . Apart from numerous muscles interferences 16,17 , the strongest one is the maternal electrocardiogram (MECG) of a relatively large amplitude [18][19][20] . Owing to incomplete MECG suppression [21][22][23][24] , the FECG signal becomes unsuitable for further analysis, thus preventing the detection of FQRS complexes, precise location of R waves and FHR determination [25][26][27] .…”

Section: Background and Summarymentioning

confidence: 99%

“…Undoubtedly, the evaluation of its usefulness requires, above all, the effectiveness of the FQRS detection to be measured, which directly affects the continuity of FHR signal determination 18,26 . It depends on the entire abdominal signal processing channel: interference filtering, maternal electrocardiogram suppression and FQRS complexes detection 17,39,56 . The effectiveness estimates the usefulness of the FHR signal based on a non-invasive FECG for classic assessment of heart rate variability.…”

Section: Technical Validationmentioning

confidence: 99%

See 1 more Smart Citation

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Section: Technical Validationmentioning

confidence: 99%

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In order to assess wavelet denoising performance on the real dataset, three different quantitative measures were employed. The first one is the SNR, which was evaluated as: SNR dB = 20 log 10 App f 4σ (4) where σ is the standard deviation of the noise and App f represents the peak-to-peak amplitude of the average fetal QRS complex. The average QRS complex was calculated only on highly correlated fetal beats, considering a Pearson's correlation coefficient value higher than 0.6.…”

Section: Comparative Analysis and Metricsmentioning

confidence: 99%

“…The oldest and most widely used technique is the averaging of the consecutive ECG complexes [2], [3]. In [4], the time-sequenced adaptive filter was applied to enhance the quality of multichannel fECG after the maternal ECG had been removed. In [5], the enhancements achievable by the application of wavelet transform to fECG signals extracted by polynomial networks were presented.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based Post-processing Methods for the Enhancement of Non-invasive Fetal ECG

Baldazzi¹,

Sulas²,

Brungiu³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

Despite the number of techniques developed in the literature, the extraction of a clean fetal ECG (fECG) from non-invasive recordings is still an open research issue. In this work, different wavelet-based post-processing approaches for the denoising of the fECG were evaluated. A small dataset composed of twenty signals recorded from ten pregnant women between the 21st and the 27th week of gestation was adopted. fECG extraction was accomplished by using a multireference QR-decomposition-based recursive least squares adaptive filter. Then, all signals were decomposed with the stationary wavelet transform (SWT) and stationary wavelet packet transform (SWPT), using a 7-level decomposition with Haar mother wavelet and hard-thresholding. Two different thresholds from the literature were tested: the first one is level-independent (Minimax) while the other one is level-dependent. The latter was adapted to be exploited on SWPT. The enhancement of the fetal QRS complex was analyzed by computing the improvement of the signal-to-noise ratio and the performance of a fetal QRS detector. The comparative analysis revealed how the SWT outperforms the more complex SWPT, regardless the thresholding approach. Figure 2. Example of SWT and SWPT denoising results with 7-level decomposition. From top to bottom: raw abdominal signal, SWT (Han et al.), SWT (Minimax), SWPT (Han et al.), SWPT (Minimax). On the right, a one-second zoom of each signal.

show abstract

“…The running average of several heartbeats is widely used to improve the SNR of the signal at the expense of losing individual variations in pulse shape. In a previous work, the authors have used an augmented time-sequenced adaptive filter as a post-processing quality enhancement step [2]. However, the fetal pulse locations are needed to synchronise the filter and additionally the filter is unable to track rapid changes in the signal morphology, for instance in the presence of ectopic beats.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Encoder-Decoder Framework for Fetal ECG Signal Denoising

Fotiadou¹,

Konopczyński²,

Hesser³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

Non-invasive fetal electrocardiography has the potential of providing vital information for evaluating the health status of the fetus. However, the low signal-tonoise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Residual noise in the fetal ECG, after the maternal ECG is suppressed, is often non-stationary, complex and has spectral overlap with the fetal ECG. We present a deep fully convolutional encoder-decoder framework, for removing the residual noise from single-channel fetal ECG. The method was tested in a broad simulated fetal ECG dataset with varying amount of noise. The results demonstrate that after the denoising there was an average increase in the correlation coefficient between the corrupted signals and the original ones from 0.6 to 0.8. Moreover, the suggested framework successfully handled different levels of noises in a single model. The network was further tested on real signals showing substantial noise removal performance, thus providing a promising approach for fetal ECG signal denoising. The presented method is able to significantly improve the quality of the extracted fetal ECG signals, having the advantage of preserving beat-to-beat morphological variations.

show abstract

Enhancement of low-quality fetal electrocardiogram based on time-sequenced adaptive filtering

Cited by 29 publications

References 34 publications

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Wavelet-based Post-processing Methods for the Enhancement of Non-invasive Fetal ECG

Deep Convolutional Encoder-Decoder Framework for Fetal ECG Signal Denoising

Contact Info

Product

Resources

About