R Rik Vullings

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%).

Power spectrum analysis of fetal heart rate variability at near term and post term gestation during active sleep and quiet sleep

BACKGROUND Spectral analysis of fetal heart rate variability is promising for assessing fetal condition. Before using spectral analysis for fetal monitoring it has to be determined whether there should be a correction for gestational age or behavioural state. AIMS Compare spectral values of heart rate variability between near term and post term fetuses during active and quiet sleep. STUDY DESIGN Case-control. Cases had a gestational age of > or =42 weeks; controls were 36 to 37 weeks. Fetuses were matched for birth weight percentile. SUBJECTS STAN registrations from healthy fetuses. For each fetus one 5-minute segment was selected during active and one during quiet sleep. OUTCOME MEASURES Absolute and normalized low (0.04-0.15 Hz) and high frequency power (0.4-1.5 Hz) of heart rate variability. RESULTS Twenty fetuses were included. No significant differences were found between cases and controls in absolute (481 and 429 respectively, P=0.88) or normalized low (0.78 and 0.80 respectively, P=0.50) or absolute (41 and 21 respectively, P=0.23) or normalized high frequency power (0.08 and 0.07 respectively, P=0.20) during active state. During rest, normalized low frequency power was lower (0.58 and 0.69 respectively, P=0.03) and absolute (16 and 10 respectively, P=0.04) and normalized high frequency power were higher (0.21 and 0.14 respectively, P=0.01) in cases compared to controls. Absolute and normalized low frequency power were higher during active state compared to rest in both groups (all P values <0.05). CONCLUSIONS We found sympathetic predominance during active state in fetuses around term. Post term parasympathetic modulation during rest was increased compared to near term.

Motion Artifacts in Capacitive ECG Measurements: Reducing the Combined Effect of DC Voltages and Capacitance Changes Using an Injection Signal

Capacitive electrodes are a promising alternative to the conventional adhesive electrodes for ECG measurements. They provide more comfort to the patient when integrated in everyday objects (e.g., beds or seats) for long-term monitoring. However, the application of capacitive sensors is limited by their high sensitivity to motion artifacts. For example, motion at the body-electrode interface causes variations of the coupling capacitance which, in the presence of a dc voltage across the coupling capacitor, create strong artifacts in the measurements. The origin, relevance, and reduction of this specific and important type of artifacts are studied here. An injection signal is exploited to track the variations of the coupling capacitance in real time. This information is then used by an identification scheme to estimate the artifacts and subtract them from the measurements. The method was evaluated in simulations, lab environments, and in a real-life recording on an adults chest. For the type of artifact under study, a strong artifact reduction ranging from 40 dB for simulated data to 9 dB for a given real-life recording was achieved. The proposed method is automated, does not require any knowledge about the measurement system parameters, and provides an online estimate for the dc voltage across the coupling capacitor.

Fetal heart rate variability during pregnancy, obtained from non-invasive electrocardiogram recordings

Non‐invasive spectral analysis of fetal heart rate variability is a promising new field of fetal monitoring. To validate this method properly, we studied the relationship between gestational age and the influence of fetal rest–activity state on spectral estimates of fetal heart rate variability.

Heart Rate Detection in Low Amplitude Non-Invasive Fetal ECG Recordings

Multi-electrode electrical measurements on the maternal abdomen may provide a valuable alternative to standard fetal monitoring. Removal of the maternal ECG from these recordings by means of subtracting a weighted linear combination of segments from preceding maternal ECG complexes, results in fetal ECG traces from which the fetal heart rate can be determined. Unfortunately, these traces often contain too much noise to determine the heart rate by R-peak detection. To overcome this limitation, an algorithm has been developed that calculates the heart rate based on cross-correlation. To validate the algorithm, noise was added to a fetal scalp ECG recording to simulate low amplitude abdominal recordings. Heart rates calculated by the algorithm were compared to the heart rates from the original scalp ECG. For simulated signals with a signal to noise ratio of 2, the coefficient of correlation was 0.99 (p<0.001). By using the developed algorithm for calculating the fetal heart rate, multi-electrode electrical measurements on the maternal abdomen now can be used for fetal monitoring in relatively early stages of pregnancy or other situations where ECG amplitudes are low or noise levels are high

Maternal ECG removal from non-invasive fetal ECG recordings

Fetal monitoring during pregnancy is important to support medical decision making. The fetal electrocardiogram (fECG) is a valuable signal to diagnose fetal well-being. Non-invasive recording of the fECG is performed by positioning electrodes on the maternal abdomen. The signal to noise ratio of these recordings is relatively low and the main undesired signal is the maternal electrocardiogram (mECG). Existing methods to remove the mECG signal are not sufficiently accurate to extract the complete fECG signal. In this paper, a novel method for removal of the mECG signal from abdominal recordings is presented. It is an extension of the linear prediction method. Each mECG complex is segmented and these segments are separately estimated by linear prediction. Both the presented method and the standard linear prediction are applied to simulated abdominal recordings and evaluated by determining the rms errors between the estimated and the actual fECG signals. The ratio between the rms errors of the linear prediction method and the presented method varies between 0.4 dB and 2.3 dB. It can therefore be concluded that the presented method is capable of a more accurate removal of the mECG signal for all simulated abdominal recordings with respect to the linear prediction method

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.

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.