Fetal Cardiac Signal Research Articles

PO-01-207 VERNIX CASEOSA DISTRIBUTION MODULATES NON-INVASIVE FETAL ECG SIGNAL: A COMPUTATIONAL STUDY

Non-invasive fetal ECG (FECG) shows promise as a diagnostic method to monitor fetal health. However, extracting the fetal cardiac signal remains a challenge due to low signal-to-noise ratio. An important factor is a low-conducting layer which forms around the fetus from week 28 of gestation called the vernix caseosa (VC). We developed an anatomically detailed and biophysically accurate computational model of a pregnant torso to investigate the effects of the VC on the FECG. The model was constructed by combining geometries of a pregnant woman with a 24 week infant and a torso with ventricles (a). Ionic current properties of the maternal heart is modelled using the Ten Tusscher human ventricular cardiomyocyte model, while a modified version adapted to match fetal electrophysiology is used for the fetal heart. 22 distribution patterns of the VC was generated using combinations of 3 regions and 4 thickness. No VC case was used as control. Electrical propagation was simulated using the pseudo-bidomain formulation. Sinus rhythm was simulated with a baseline cycle of 500 ms and 450 ms for the maternal and fetal hearts, respectively. When VC was represented as homogeneous, changing the VC thickness from 0.5 mm to 3 mm resulted in reduction of QRS maximum amplitude from 6 x 10-5 to 6 x 10-6 mV (c). However, in the heterogeneous VC models, there is no significant difference in signal as thickness was changed (d). The location of the VC significantly affected the signal (e). VC only covering the head was almost identical to control with a relative difference measure of only 1.35%. However, introduction of VC on the back resulted in greatest difference in potential distribution with a relative difference measure ranging between 34.5 and 49%. We have developed a novel, highly-detailed computational model of a pregnant torso that shows how VC distribution affects the FECG. This model can be used to improve techniques in extracting FECG at different gestational stages.

Can fetal heart rate variability obtained from cardiotocography provide the same diagnostic value like from electrophysiological interbeat intervals?

Objective. Fetal heart rate variability (HRV) is widely used for monitoring fetal developmental disturbances. Only expensive fetal magnetocardiography (fMCG) allows the precise recording of the individual fetal heart beat intervals uncovering also highly frequent vagal modulation. In contrast, transabdominal fetal electrocardiography (fECG) suffers from noise overlaying the fetal cardiac signal. Cardiotocography (CTG) is the clinical method of choice, however, based on Doppler ultrasound, improper to resolve single beats concisely. The present work addresses the transferability of established electrophysiological HRV indices to CTG recordings during the fetal maturation period of 20–40 weeks of gestation (WGA). Approach. We compared (a) HRV indices obtained from fMCG, CTG and fECG of short-term amplitude fluctuations (sAMPs) and long-term amplitude fluctuations (lAMPs) and complexity, and (b) their diagnostic value for identifying maturational age, fetal growth restriction (FGR) and small for gestational age (SGA). We used the functional brain age score (fABAS) and categories of long- and short-term regulation and complexity. Main results. Integrating all substudies, we found: (a) indices related to long-term regulation, and with modified meaning and values of short-term regulation and sympathovagal balance (SVB) according to electrophysiological HRV standards can be obtained from CTG. (b) Models using HRV indices calculated from CTG allow the identification of maturational age and discriminate FGR from controls with almost similar precision as electrophysiological means. (c) A modified set of HRV parameters containing short- and long-term regulation and long-term/short-term ratio appeared to be most suitable to describe autonomic developmental state when CTG data is used. Significance. Whereas the predominantly vagally modulated beat-to-beat precise high frequencies of HRV are not assessable from CTG, we identified relevant related HRV indices and categories for CTG recordings with diagnostic potential. They require further evaluation and confirmation with respect to any issues of fetal developmental and perinatal problems in subsequent studies. This methodology significantly extends the measures of established CTG devices. Novelty and significance HRV indices provide predestinated diagnostic markers of autonomic control in fetuses. However, the established CTG does not provide the temporal precision of electrophysiological recordings.Beat-to-beat related, mainly vagally modulated behavior is not exactly represented in CTG. However, a set of CTG-specific HRV indices that are mainly comparable to established electrophysiological HRV parameters obtained by magnetocardiography or electrocardiography provided almost similar predictive value for fetal maturational age and were helpful in characterizing FGR.These results require validation in the monitoring of further fetal developmental disturbances.We recommend a corresponding extension of CTG methodology.

Fetal Cardiac Doppler Signal Processing Techniques: Challenges and Future Research Directions.

The fetal Doppler Ultrasound (DUS) is commonly used for monitoring fetal heart rate and can also be used for identifying the event timings of fetal cardiac valve motions. In early-stage fetuses, the detected Doppler signal suffers from noise and signal loss due to the fetal movements and changing fetal location during the measurement procedure. The fetal cardiac intervals, which can be estimated by measuring the fetal cardiac event timings, are the most important markers of fetal development and well-being. To advance DUS-based fetal monitoring methods, several powerful and well-advanced signal processing and machine learning methods have recently been developed. This review provides an overview of the existing techniques used in fetal cardiac activity monitoring and a comprehensive survey on fetal cardiac Doppler signal processing frameworks. The review is structured with a focus on their shortcomings and advantages, which helps in understanding fetal Doppler cardiogram signal processing methods and the related Doppler signal analysis procedures by providing valuable clinical information. Finally, a set of recommendations are suggested for future research directions and the use of fetal cardiac Doppler signal analysis, processing, and modeling to address the underlying challenges.

Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems

Background and ObjectiveWe proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. MethodsThe method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. ResultsIn environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. ConclusionsThe results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its capability to capture the nonlinearities of the model better.

Motion compensated cine CMR of the fetal heart using radial undersampling and compressed sensing

BackgroundTo develop and evaluate a reconstruction framework for high resolution time-resolved CMR of the fetal heart in the presence of motion.MethodsData were acquired using a golden angle radial trajectory in seven fetal subjects and reconstructed as real-time images to detect fetal movement. Data acquired during through-plane motion were discarded whereas in-plane motion was corrected. A fetal cardiac gating signal was extracted to sort the corrected data by cardiac phase, allowing reconstruction of cine images. The quality of motion corrected images and the effect of data undersampling were quantified using separate expressions for spatial blur and image error.ResultsMotion corrected reordered cine reconstructions (127 slices) showed improved image quality relative to both uncorrected cines and corresponding real-time images across a range of root-mean-squared (RMS) displacements (0.3–3.7 mm) and fetal heart rates (119–176 bpm). The relative spatial blur between cines with and without motion correction increased with in-plane RMS displacement leading to an effective decrease in the effective spatial resolution for images without motion correction. Image error between undersampled and reference images was less than 10% for reconstructions using 750 or more spokes, yielding a minimum acceptable scan time of approximately 4 s/slice during quiescent through plane motion.ConclusionsBy rejecting data corrupted by through-plane motion, and correcting data corrupted by in-plane translation, the proposed reconstruction framework accounts for common sources of motion artifact (gross fetal movement, maternal respiration, fetal cardiac contraction) to produce high quality images of the fetal heart.

Computerized analysis of fetal heart rate variability signal during the stages of labor.

To analyze computerized cardiotocographic (cCTG) parameters (baseline fetal heart rate, baseline FHR; short term variability, STV; approximate entropy, ApEn; low frequency, LF; movement frequency, MF; high frequency, HF) in physiological pregnancy in order to correlate them with the stages of labor. This could provide more information for understanding the mechanisms of nervous system control of FHR during labor progression. A total of 534 pregnant women were monitored on cCTG from the 37th week before the onset of spontaneous labor and during the first and the second stage of labor. Statistical analysis was performed using Kruskal-Wallis test and Wilcoxon rank-sum test with the Bonferroni adjusted α (< 0.05). Statistically significant differences were seen between baseline FHR, MF and HF (P < 0.001), in which the first two were reduced and the third was increased when compared between pre-labor, and the first and second stages of labor. Differences between some of the stages were found for ApEn, LF and for LF/(HF + MF), where the first and the third were reduced and the second was increased. cCTG modifications during labor may reflect the physiologic increased activation of the autonomous nervous system. Using computerized fetal heart rate analysis during labor it may be possible to obtain more information from the fetal cardiac signal, in comparison with the traditional tracing.

Segmented independent component analysis for improved separation of fetal cardiac signals from nonstationary fetal magnetocardiograms.

Fetal magnetocardiograms (fMCGs) have been successfully processed with independent component analysis (ICA) to separate the fetal cardiac signals, but ICA effectiveness can be limited by signal nonstationarities due to fetal movements. We propose an ICA-based method to improve the quality of fetal signals separated from fMCG affected by fetal movements. This technique (SegICA) includes a procedure to detect signal nonstationarities, according to which the fMCG recordings are divided in stationary segments that are then processed with ICA. The first and second statistical moments and the signal polarity reversal were used at different threshold levels to detect signal transients. SegICA effectiveness was assessed in two fMCG datasets (with and without fetal movements) by comparing the signal-to-noise ratio (SNR) of the signals extracted with ICA and with SegICA. Results showed that the SNR of fetal signals affected by fetal movements improved with SegICA, whereas the SNR gain was negligible elsewhere. The best measure to detect signal nonstationarities of physiological origin was signal polarity reversal at threshold level 0.9. The first statistical moment also provided good results at threshold level 0.6. SegICA seems a promising method to separate fetal cardiac signals of improved quality from nonstationary fMCG recordings affected by fetal movements.

A Deflation Procedure for Subspace Decomposition

A general deflation framework is described for the separation of a desired signal subspace of arbitrary dimensions from noisy multichannel observations. The method simultaneously uses single and multichannel priors to split the desired and undesired subspaces, even for coplanar (intersecting) subspaces. By appropriate use of signal priors, it can even extract signals from degenerate mixtures of signals and noise recorded from a few number of channels in low SNR scenarios, without the reduction of the data dimensions. As a case study, the performance of the proposed method is studied for the problem of extracting fetal cardiac signals from maternal abdominal recordings, over simulated and real data. A second case study deals with the degenerate problem of extracting diaphragmatic electromyogram from electrocardiograph artifacts. A provisional patent application based on this method has been filed.

Fetal magnetocardiographic data processing

Event Abstract Back to Event Fetal magnetocardiographic data processing Silvia Comani1* 1 University of Chieti, Italy Fetal Magnetocardiography (fMCG) has been used to detect the fetal cardiac activity at different gestational ages and has progressively shown its potential for the non-invasive monitoring of the fetal heart function and well-being. Several signals are mixed in fMCG recordings, among which ambient noise and maternal cardiac activity primarily need to be suppressed because they may hinder the fetal cardiac signal. Among the several methods that have been used to disentangle the fetal cardiac signal, we employed independent component analysis (ICA) to obtain good separation of the fetal cardiac signals from fMCG data sets acquired at different gestational ages (15 to 38 weeks). Several factors, such as the identification of the fetal heartbeats, the signal-to-noise ratio of the fetal traces, the quality of the fetal signal waveform, the amount of residual maternal contamination in the fetal traces, can be evaluated to assess method performance. Once the fetal cardiac signal is separated, non linear parameters, such as entropy, can be evaluated to highlight the non linear behavior of the fetal heart along gestation. Moreover, the dynamical properties of the fetal cardiac system can be investigated by determining the properties of the attractors in the phase space, and by calculating the correlation dimensions and the Lyapunov exponents (LE) of the cardiovascular system along gestation. Those parameters permit to measure the rate of convergence or divergence of nearby trajectories of the dynamical system in the phase space, and can be considered dynamical indicators of chaos. Furthermore, the distribution of the LE can provide information on the possible deterministic nature of the system and on the number of degrees of freedom involved in the collected experimental data. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Oral Presentation Topic: Fetal and neonatal biomagnetism Citation: Comani S (2010). Fetal magnetocardiographic data processing. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00093 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Mar 2010; Published Online: 23 Mar 2010. * Correspondence: Silvia Comani, University of Chieti, Chieti, Italy, comani@unich.it Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Silvia Comani Google Silvia Comani Google Scholar Silvia Comani PubMed Silvia Comani Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Quantifying Fetal Evoked Responses using Spectral Coherence Measure

Event Abstract Back to Event Quantifying Fetal Evoked Responses using Spectral Coherence Measure Rathinaswamy Govindan1*, Hubert Preissl2, 3, Pamela Murphy1, Maureen Ware1, Curtis Lowery1 and Hari Eswaran1 1 University of Arkansas for Medical Sciences, Department of Obstetrics and Gynaecology, United States 2 University of Tubingen, MEG-Center,University Hospital, Germany 3 University of Arkansas, Department for Obstetrics and Gynecology, Medical College, United States Traditionally, brain evoked responses (ER) are quantified by performing event-related averaging. However, this approach has limitations if applied to fetal brain signals to quantify the ER because of their small amplitude and interference from other signals such as fetal movements. In order to quantify the fetal ER, one of the following approaches can be taken: (i) non-parametric approach such as bootstrap to assess the significance of ER (ii) use of parametric approach. Here we propose to use spectral coherence to quantify ER. For this purpose we collect auditory ER (AER) using a 151 SQUID array system. Using signal space projection technique, we attenuate the interfering maternal and fetal cardiac signals. To this end, we compute the spectral coherence (SC) between the trigger series and the fetal magentoencephalogram (fMEG) signals. SC is computed as the ratio of the estimate of the cross-spectrum between the trigger signal and fMEG to product of the auto-spectra of the two signals. The value of coherence above the pre-defined significance level is considered as significant coherence (i.e. AER). For the sensors displaying significant coherence the latency of the ER is computed using the phase spectrum, which is defined by the cross-spectrum. Latency is computed by normalizing the phase value in the frequency band of significant coherence by that frequency itself. In this work we demonstrate the application of this approach to serially recorded AER from fetuses at different gestation age and discuss their neurological maturation.

An Expectation–Maximization Method for Spatio–Temporal Blind Source Separation Using an AR-MOG Source Model

In this paper, we develop a maximum-likelihood (ML) spatio-temporal blind source separation (BSS) algorithm, where the temporal dependencies are explained by assuming that each source is an autoregressive (AR) process and the distribution of the associated independent identically distributed (i.i.d.) innovations process is described using a mixture of Gaussians. Unlike most ML methods, the proposed algorithm takes into account both spatial and temporal information, optimization is performed using the expectation-maximization (EM) method, the source model is adapted to maximize the likelihood, and the update equations have a simple, analytical form. The proposed method, which we refer to as autoregressive mixture of Gaussians (AR-MOG), outperforms nine other methods for artificial mixtures of real audio. We also show results for using AR-MOG to extract the fetal cardiac signal from real magnetocardiographic (MCG) data.

Validation of the flash-evoked response from fetal MEG

Flash-evoked responses can be recorded from the fetus in utero. However, a standard analysis approach based on orthogonal projection (OP) to attenuate maternal and fetal cardiac signals leads to a spatial redistribution of the signal. This effect prevents the correlation of source location with a known fetal head location in some cases and the signal-to-noise ratio (SNR) is sometimes limited such that the response latency is difficult to determine. We used a modified beamformer model search analysis to avoid the redistribution shortcoming and to improve the SNR. We included a statistical test for residual interference in the average and quantified significance of the evoked response with a bootstrap method. Selected source locations compared favorably to fetal head locations estimated from ultrasound exams. The evoked response time course was found to have a significant post-trigger peak with a latency between about 180 and 770 ms in more than 90% of the subject measurements. These results confirm that the combined application of a beamformer model search and bootstrap significance test provides a validation of the flash-evoked response observed in OP processed fetal MEG channels.

Detection and analysis of fetal arrhythmia in a single twin fetus using magnetocardiography

Abstract An important clinical application of fetal magnetocardiography (FMCG) is the identification and classification of fetal arrhythmias. However, separation of fetal and maternal signals can be challenging due to cardiac conduction disturbances and changes in signal morphology. In twin pregnancy, the presence of an additional cardiac signal further complicates data processing. We report on the processing of FMCG signals obtained in twin pregnancy in the 28th week with arrhythmia in a single fetus. A 12-min FMCG was recorded using a 61-channel biomagnetometer with sufficient coverage to simultaneously register both fetal cardiac signals. Using signal template matching, digital averaging and subtracting techniques, the cardiac signals of the mother and both fetuses could be separately identified and displayed. Examination of the timing of ventricular complexes in combination with their signal morphologies permitted the identification and classification of arrhythmic events. Two episodes of supraventricular tachycardia over total period of 3 min at heart rates around 280 bpm were present. The remaining trace showed intermittent changes between sinus beats, supraventricular extrasystoles and compensatory pauses as well as blocked atrial beats. We conclude that appropriate signal processing techniques permits the clinical interpretation of FMCG signals even in complex spatio–temporal combinations of signals.

Fetal magnetocardiography in an unshielded environment

Abstract We investigated the viability of a 2-channel low temperature biomagnetometer system implementing 2nd order gradiometers in an unshielded environment. We examined 2 fetuses (30th, 38th week of gestation) in and outside a shielded room as well as 3 (weeks 32, 36, 37) in the unshielded environment of an obstetrics department. Reference fetal magnetocardiograms (FMCG) were registered in a shielded room using a 61-channel biomagnetometer. In the hospital, fetal heart position was estimated on the basis of sonography. Inside the shielded room, fetal cardiac signals were successfully registered by the 2-channel system and were comparable to those of the multichannel system. Outside the shielded room, only signals of the older fetus were identifiable (signal-to-noise ratio: 7 inside, 2 outside the shielded room). In the hospital, 5 to 8 measurements per fetus were performed. In one fetus (37th week), online cardiac signals could be obtained intermittently. This report of unshielded FMCG acquisition confirms that it is possible to register FMCGs in late pregnancy in an unshielded environment. However, further improvements in noise reduction are necessary to permit routine FMCG acquisition in hospital environments.

Fetal cardiac signal extraction from magnetocardiographic data using a probabilistic algorithm

Fetal magnetocardiographic sensor measurements are contaminated by undesired environmental and biological signals, such as the maternal cardiac signal. Several methods have been used in an attempt to extract the fetal cardiac signal from these data, which are based on, e.g., the presumed quasi-periodicity of the maternal cardiac signal or the presumed statistical independence between the fetal cardiac signal and interfering signals. Recently a different type of method for extracting signals from noisy data has been introduced. This probabilistic method, known as partitioned factor analysis (PFA), assumes that the data can be partitioned into periods of source inactivity and source activity. PFA was originally developed for stimulus-evoked, trial-averaged encephalographic data, for which the partitions are known in advance. Here we show how to use PFA for extracting the fetal cardiac signal from cardiographic data, for which the partitions must be determined from the data. In addition, we show that PFA can be used even when the partitions cannot be determined directly from the data.

Entropy-based automated classification of independent components separated from fMCG

Fetal magnetocardiography (fMCG) is a noninvasive technique suitable for the prenatal diagnosis of the fetal heart function. Reliable fetal cardiac signals can be reconstructed from multi-channel fMCG recordings by means of independent component analysis (ICA). However, the identification of the separated components is usually accomplished by visual inspection. This paper discusses a novel automated system based on entropy estimators, namely approximate entropy (ApEn) and sample entropy (SampEn), for the classification of independent components (ICs). The system was validated on 40 fMCG datasets of normal fetuses with the gestational age ranging from 22 to 37 weeks. Both ApEn and SampEn were able to measure the stability and predictability of the physiological signals separated with ICA, and the entropy values of the three categories were significantly different at p <0.01. The system performances were compared with those of a method based on the analysis of the time and frequency content of the components. The outcomes of this study showed a superior performance of the entropy-based system, in particular for early gestation, with an overall ICs detection rate of 98.75% and 97.92% for ApEn and SampEn respectively, as against a value of 94.50% obtained with the time-frequency-based system.

Performance comparison of six independent components analysis algorithms for fetal signal extraction from real fMCG data

In this study we compare the performance of six independent components analysis (ICA) algorithms on 16 real fetal magnetocardiographic (fMCG) datasets for the application of extracting the fetal cardiac signal. We also compare the extraction results for real data with the results previously obtained for synthetic data. The six ICA algorithms are FastICA, CubICA, JADE, Infomax, MRMI-SIG and TDSEP. The results obtained using real fMCG data indicate that the FastICA method consistently outperforms the others in regard to separation quality and that the performance of an ICA method that uses temporal information suffers in the presence of noise. These two results confirm the previous results obtained using synthetic fMCG data. There were also two notable differences between the studies based on real and synthetic data. The differences are that all six ICA algorithms are independent of gestational age and sensor dimensionality for synthetic data, but depend on gestational age and sensor dimensionality for real data. It is possible to explain these differences by assuming that the number of point sources needed to completely explain the data is larger than the dimensionality used in the ICA extraction.

Fetal cardiac time intervals estimated on fetal magnetocardiograms: single cycle analysis versus average beat inspection

Fetal cardiac time intervals (fCTI) are dependent on fetal growth and development, and may reveal useful information for fetuses affected by growth retardation, structural cardiac defects or long QT syndrome. Fetal cardiac signals with a signal-to-noise ratio (SNR) of at least 15 dB were retrieved from fetal magnetocardiography (fMCG) datasets with a system based on independent component analysis (ICA). An automatic method was used to detect the onset and offset of the cardiac waves on single cardiac cycles of each signal, and the fCTI were quantified for each heartbeat; long rhythm strips were used to calculate average fCTI and their variability for single fetal cardiac signals. The aim of this work was to compare the outcomes of this system with the estimates of fCTI obtained with a classical method based on the visual inspection of averaged beats. No fCTI variability can be measured from averaged beats. A total of 25 fMCG datasets (fetal age from 22 to 37 weeks) were evaluated, and 1768 cardiac cycles were used to compute fCTI. The real differences between the values obtained with a single cycle analysis and visual inspection of averaged beats were very small for all fCTI. They were comparable with signal resolution (±1 ms) for QRS complex and QT interval, and always <5 ms for the PR interval, ST segment and T wave. The coefficients of determination between the fCTI estimated with the two methods ranged between 0.743 and 0.917. Conversely, inter-observer differences were larger, and the related coefficients of determination ranged between 0.463 and 0.807, assessing the high performance of the automated single cycle analysis, which is also rapid and unaffected by observer-dependent bias.

Performance comparison of independent component analysis algorithms for fetal cardiac signal reconstruction: a study on synthetic fMCG data

Independent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithms (FastICA, CubICA, JADE, Infomax, TDSEP and MRMI-SIG) for fetal magnetocardiography (fMCG). Synthetic datasets were used to check the quality of the separated components against the original traces. Real fMCG recordings were simulated with linear combinations of typical fMCG source signals: maternal and fetal cardiac activity, ambient noise, maternal respiration, sensor spikes and thermal noise. Clusters of different dimensions (19, 36 and 55 sensors) were prepared to represent different MCG systems. Two types of signal-to-interference ratios (SIR) were measured. The first involves averaging over all estimated components and the second is based solely on the fetal trace. The computation time to reach a minimum of 20 dB SIR was measured for all six algorithms. No significant dependency on gestational age or cluster dimension was observed. Infomax performed poorly when a sub-Gaussian source was included; TDSEP and MRMI-SIG were sensitive to additive noise, whereas FastICA, CubICA and JADE showed the best performances. Of all six methods considered, FastICA had the best overall performance in terms of both separation quality and computation times.

Fetal evaluation with ultrasonic Doppler cardiac isovolumetric contraction time

AbstractThe measurement of isovolumetric contraction time (ICT) has been established as a method to evaluate cardiac function. In the fetus, the ICT is calculated from each valve signal obtained by Doppler cardiogram, noninvasively. Recently, the suitable digital-filter setting dividing fetal Doppler cardiac signal enabled us to detect fetal ICT more accurately. In fetal lamb study with this digital band-pass filter, it has been reported that as fetal acidosis developed, a drastic prolongation of the ICT occurred. A prolongation of ICT in potentially compromised fetuses predicted adverse fetal outcome, probably because acidemia following chronic hypoxemia could affect myocardial contractility in human fetuses. Fetal ICT obtained by Doppler cardiac signal is regarded as a simplified and useful index for clinical use in the assessment of fetal cardiac function.