J.W.M. Bergmans

An Improved Adaptive Power Line Interference Canceller for Electrocardiography

Power line interference may severely corrupt a biomedical recording. Notch filters and adaptive cancellers have been suggested to suppress this interference. We propose an improved adaptive canceller for the reduction of the fundamental power line interference component and harmonics in electrocardiogram (ECG) recordings. The method tracks the amplitude, phase, and frequency of all the interference components for power line frequency deviations up to about 4 Hz. A comparison is made between the performance of our method, former adaptive cancellers, and a narrow and a wide notch filter in suppressing the fundamental power line interference component. For this purpose a real ECG signal is corrupted by an artificial power line interference signal. The cleaned signal after applying all methods is compared with the original ECG signal. Our improved adaptive canceller shows a signal-to-power-line-interference ratio for the fundamental component up to 30 dB higher than that produced by the other methods. Moreover, our method is also effective for the suppression of the harmonics of the power line interference

Dynamic segmentation and linear prediction for maternal ECG removal in antenatal abdominal recordings

Monitoring the fetal heart rate (fHR) and fetal electrocardiogram (fECG) during pregnancy is important to support medical decision making. Before labor, the fHR is usually monitored using Doppler ultrasound. This method is inaccurate and therefore of limited clinical value. During labor, the fHR can be monitored more accurately using an invasive electrode; this method also enables monitoring of the fECG. Antenatally, the fECG and fHR can also be monitored using electrodes on the maternal abdomen. The signal-to-noise ratio of these recordings is, however, low, the maternal electrocardiogram (mECG) being the main interference. Existing techniques to remove the mECG from these non-invasive recordings are insufficiently accurate or do not provide all spatial information of the fECG. In this paper a new technique for mECG removal in antenatal abdominal recordings is presented. This technique operates by the linear prediction of each separate wave in the mECG. Its performance in mECG removal and fHR detection is evaluated by comparison with spatial filtering, adaptive filtering, template subtraction and independent component analysis techniques. The new technique outperforms the other techniques in both mECG removal and fHR detection (by more than 3%).

Fetal autonomic response to severe acidaemia during labour

Please cite this paper as: van Laar J, Peters C, Vullings R, Houterman S, Bergmans J, Oei S. Fetal autonomic response to severe acidaemia during labour. BJOG 2010;117:429–437.

Fetal ECG extraction during labor using an adaptive maternal beat subtraction technique.

Abstract Fetal ECG (FECG) monitoring using abdominal maternal signals is a non-invasive technique that allows early detection of changes in fetal wellbeing. Several other signal components have stronger energy than the FECG, the most important being maternal ECG (MECG) and, especially during labor, uterine EMG. This study proposes a new method to subtract MECG after detecting and removing abdominal signal segments with high-amplitude variations due to uterine contractions. The method removes MECG from abdominal signals using an approximation of the current MECG segment based on a linear combination of previous MECG segments aligned on the R-peak. The coefficients of the linear model are computed so that the squared error of the approximation over the whole current segment is minimized. Abdominal signal segments strongly affected by uterine contractions are detected by applying median filtering. The methods proposed are tested on real abdominal data recorded during labor, with FECG recorded using scalp electrodes synchronously recorded for comparison.

A robust physiology-based source separation method for QRS detection in low amplitude fetal ECG recordings

The use of the non-invasively obtained fetal electrocardiogram (ECG) in fetal monitoring is complicated by the low signal-to-noise ratio (SNR) of ECG signals. Even after removal of the predominant interference (i.e. the maternal ECG), the SNR is generally too low for medical diagnostics, and hence additional signal processing is still required. To this end, several methods for exploiting the spatial correlation of multi-channel fetal ECG recordings from the maternal abdomen have been proposed in the literature, of which principal component analysis (PCA) and independent component analysis (ICA) are the most prominent. Both PCA and ICA, however, suffer from the drawback that they are blind source separation (BSS) techniques and as such suboptimum in that they do not consider a priori knowledge on the abdominal electrode configuration and fetal heart activity. In this paper we propose a source separation technique that is based on the physiology of the fetal heart and on the knowledge of the electrode configuration. This technique operates by calculating the spatial fetal vectorcardiogram (VCG) and approximating the VCG for several overlayed heartbeats by an ellipse. By subsequently projecting the VCG onto the long axis of this ellipse, a source signal of the fetal ECG can be obtained. To evaluate the developed technique, its performance is compared to that of both PCA and ICA and to that of augmented versions of these techniques (aPCA and aICA; PCA and ICA applied on preprocessed signals) in generating a fetal ECG source signal with enhanced SNR that can be used to detect fetal QRS complexes. The evaluation shows that the developed source separation technique performs slightly better than aPCA and aICA and outperforms PCA and ICA and has the main advantage that, with respect to aPCA/PCA and aICA/ICA, it performs more robustly. This advantage renders it favorable for employment in automated, real-time fetal monitoring applications.

Improved Method for Fetal Heart Rate Monitoring

The fetal ECG can be detected in the recorded abdominal signals. A new procedure to compute the fetal heart rate (FHR) is proposed. The abdominal signal is first preprocessed in order to remove the baseline and the uterine contractions. Then the ECG of the mother (MECG) is removed using coherent averaging and optimizing the averaged MECG template. The channels containing the clearest fetal ECG signal (FECG) are identified by the autocorrelation function. The FECG is enhanced by the cross correlation between the two channels that show the strongest FECG. This enhancement is possible since the residual noise in the abdominal signal after removal of baseline, uterine contractions and maternal ECG is not correlated among the channels. The fetal R-peaks are then detected and the FHR is computed. The obtained FHR is further corrected, using the information about the MECG and about the FECG

Novel Bayesian Vectorcardiographic Loop Alignment for Improved Monitoring of ECG and Fetal Movement

The continuous analysis of electrocardiographic (ECG) signals is complicated by morphological variability in the ECG due to movement of the heart. By aligning vectorcardiographic loops, movement-induced ECG variations can be partly corrected for. Existing methods for loop alignment can account for loop rotation, scaling, and time delays, but they lack the possibility to include a priori information on any of these transformations, and they are unreliable in case of low-quality signals, such as fetal ECG signals. The inclusion of a priori information might aid in the robustness of loop alignment and is, hence, proposed in this paper. We provide a generic Bayesian framework to derive our loop alignment method. In this framework, existing methods can be readily derived as well, as a simplification of our method. The loop alignment is evaluated by comparing its performance in loop alignment to two existing methods, for both adult and fetal ECG recordings. For the adult ECG recordings, a quantitative performance assessment shows that the developed method outperforms the existing method in terms of robustness. For the fetal ECG recordings, it is demonstrated that the developed method can be used to correct ECG signals for movement-induced morphology changes (enabling diagnostics) and that the method is capable of classifying recorded ECG signals to periods of fetal movement or rest (p <; 0.01). This information on fetal movement can also serve as a valuable diagnostic tool.

A continuous wavelet transform-based method for time-frequency analysis of artefact-corrected heart rate variability data

Time-frequency analysis of heart rate variability (HRV) provides relevant clinical information. However, time-frequency analysis is very sensitive to artefacts. Artefacts that are present in heart rate recordings may be corrected, but this reduces the variability in the signal and therefore adversely affects the accuracy of calculated spectral estimates. To overcome this limitation of traditional techniques for time-frequency analysis, a new continuous wavelet transform (CWT)-based method was developed in which parts of the scalogram that have been affected by artefact correction are excluded from power calculations. The method was evaluated by simulating artefact correction on HRV data that were originally free of artefacts. Commonly used spectral HRV parameters were calculated by the developed method and by the short-time Fourier transform (STFT), which was used as a reference. Except for the powers in the very low-frequency and low-frequency (LF) bands, powers calculated by the STFT proved to be extremely sensitive to artefact correction. The CWT-based calculations in the high-frequency and very high-frequency bands corresponded well with their theoretical values. The standard deviations of these powers, however, increase with the number of corrected artefacts which is the result of the non-stationarity of the R-R interval series that were analysed. The powers calculated in the LF band turned out to be slightly sensitive to artefact correction, but the results were acceptable up to 20% artefact correction. Therefore, the CWT-based method provides a valuable alternative for the analysis of HRV data that cannot be guaranteed to be free of artefacts.

Bayesian Approach to Patient-Tailored Vectorcardiography

For assessment of specific cardiac pathologies, vectorcardiography is generally considered superior with respect to electrocardiography. Existing vectorcardiography methods operate by calculating the vectorcardiogram (VCG) as a fixed linear combination of ECG signals. These methods, with the inverse Dower matrix method the current standard, are therefore not flexible with respect to different body compositions and geometries. Hence, they cannot be applied with accuracy on patients that do not conform to the fixed standard. Typical examples of such patients are obese patients or fetuses. For the latter category, when recording the fetal ECG from the maternal abdomen the distance of the fetal heart with respect to the electrodes is unknown. Consequently, also the signal attenuation/transformation per electrode is not known. In this paper, a Bayesian method is developed that estimates the VCG and, to some extent, also the signal attenuation in multichannel ECG recordings from either the adult 12-lead ECG or the maternal abdomen. This is done by determining for which VCG and signal attenuation the joint probability over both these variables is maximal given the observed ECG signals. The underlying joint probability distribution is determined by assuming the ECG signals to originate from scaled VCG projections and additive noise. With this method, a VCG, tailored to each specific patient, is determined. The method is compared to the inverse Dower matrix method by applying both methods on standard 12-lead ECG recordings and evaluating the performance in predicting ECG signals from the determined VCG. In addition, to model nonstandard patients, the 12-lead ECG signals are randomly scaled and, once more, the performance in predicting ECG signals from the VCG is compared between both methods. Finally, both methods are also compared on fetal ECG signals that are obtained from the maternal abdomen. For patients conforming to the standard, both methods perform similarly, with the developed method performing marginally better. For scaled ECG signals and fetal ECG signals, the developed method significantly outperforms the inverse Dower matrix method.

Comparison and evaluation of existing methods for the extraction of low amplitude electrocardiographic signals: a possible approach to transabdominal fetal ECG

Abstract Analysis of the fetal ECG (fECG) allows physicians to detect changes in the well-being state of the fetus. But when assessing the fECG through the abdominal signals (ADS), its very low amplitude causes a problem, as the fECG representation in the ADS is buried in a mixture of other signals with stronger energy. Different methods have been proposed in the past to extract the transabdominal fECG for instantaneous fetal heart rate (fHR) computation; four representatives of them are selected for an accurate comparison of their performance in fECG extraction and in fHR estimation. A model for the ADS including all the possible disturbances is developed within this study to generate simulated data as they are required for the quantitative comparison of the algorithms. Their performances and limits considering both the enhancement of the fECG and the ability to preserve fECG morphology are analyzed using the simulated data. The results clearly show that linear methods for maternal ECG removal provide better results with respect to the extraction of the fECG morphology. The algorithms are then tested on real ADS data recorded during labor. Finally, the advantage of considering linear methods for ADS processing is discussed.