Time course reconstruction of fetal cardiac signals from fMCG: independent component analysis versus adaptive maternal beat subtraction

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

Background

EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts.

Methods

Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR.

Results

The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs).

Discussion

Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

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.

Invariant heart beat span versus variant heart beat intervals and its application to fetal ECG extraction

Background

The fundamental assumptions for various kinds of fetal electrocardiogram (fECG) extraction methods are not consistent with each other, which is a very important problem needed to be ascertained.

Methods

Based on two public databases, the regularity on ECG wave durations for normal sinus rhythm is investigated statistically. Taking the ascertained regularity as an assumption, a new fECG extraction algorithm is proposed, called Partial R-R interval Resampling (PRR).

Results

Both synthetic and real abdominal ECG signals are used to test the algorithm. The results indicate that the PRR algorithm has better performance over the whole R-R interval resampling based comb filtering method (RR) and linear template method (LP), which takes advantages of both LP and RR.

Conclusions

The final drawn conclusion is: (1) the proposition should be true that the individual’s heart beat span is invariable for normal sinus rhythm; (2) the proposed PRR fetal ECG extraction algorithm can estimate the maternal ECG (mECG) more accurately and stably even in the condition of large HRV, finally resulting in better fetal ECG extraction.

Developing fetal motor-cardiovascular coordination analyzed from multi-channel magnetocardiography

Fetal movements (FM) related heart rate accelerations (AC) are an important maturation criterion. Since Doppler-based time resolution is not sufficient for accompanying heart rate variability analysis, the work is aimed at a comprehensive FM-AC analysis using magnetocardiographic recordings from fetuses during sleep.We identify FM and AC by independent component analysis and automatic recognition algorithms. We investigate associations between FM and AC of different magnitude by means of event coincidence and time series cross-correlation over the maturation period of 21-40 weeks of gestation (WGA).FM related AC appear with increasing AC magnitude and WGA. Vice versa, AC related FM appear independent of WGA, but more frequently with increasing AC amplitude. The FM-AC correlation exists already at 21 WGA and further increases with WGA while the variability of its time delay decreases. Hence, FM and AC are clearly associated over the whole investigated maturation period. The increase of FM related AC runs parallel to the increasing AC magnitude.The MCG methodology was confirmed and results from previous Doppler-based analyses reproduced. Hence, MCG recordings allow the collective analysis of heart rate variability based maturation indices and FM related AC. This synergism may improve the diagnosis of fetal developmental disorders.

Principal component model for maternal ECG extraction in fetal QRS detection

Fetal cardiac monitoring using noninvasive abdominal leads can provide important information on fetal well-being. However, extraction of fetal electrocardiogram (fECG) from abdominal signals is often problematic because of the higher amplitude maternal ECG (mECG). The aim of this study was to introduce a principal component model for removing the maternal ECG from abdominal signals. The proposed method removes mECG waveforms with high accuracy even though abdominal movements cause morphological deviation to mECG complexes. The method can be used for single or multi-lead measurements. The proposed method was tested using 175 1 min long abdominal measurements with fetal QRS annotation markers acquired from a fetal scalp electrode. Using the presented mECG removing algorithm and matched filtering based fQRS detector, 95% sensitivity for fQRS detection and 4.84 ms RMS error for fetal RR-interval estimation were acquired.

Clinical application of magnetocardiography

Magnetocardiography is a noninvasive contactless method to measure the magnetic field generated by the same ionic currents that create the electrocardiogram. The time course of magnetocardiographic and electrocardiographic signals are similar. However, compared with surface potential recordings, multichannel magnetocardiographic mapping (MMCG) is a faster and contactless method for 3D imaging and localization of cardiac electrophysiologic phenomena with higher spatial and temporal resolution. For more than a decade, MMCG has been mostly confined to magnetically shielded rooms and considered to be at most an interesting matter for research activity. Nevertheless, an increasing number of papers have documented that magnetocardiography can also be useful to improve diagnostic accuracy. Most recently, the development of standardized instrumentations for unshielded MMCG, and its ease of use and reliability even in emergency rooms has triggered a new interest from clinicians for magnetocardiography, leading to several new installations of unshielded systems worldwide. In this review, clinical applications of magnetocardiography are summarized, focusing on major milestones, recent results of multicenter clinical trials and indicators of future developments.

The significance of the senescence pathway in breast cancer progression

Invasive breast cancer develops through prolonged accumulation of multiple genetic changes. The progression to a malignant phenotype requires overriding of growth inhibition. It is evident that some breast cancers have an inherited basis, and both hereditary and sporadic cancers appear to involve molecular mechanisms that are linked to the cell cycle. Frequently, changes in the molecular pathways with gene deletions, point mutations and/or overexpression of growth factors can be seen in these cancers. Recent evidence also implicates the senescence pathway in breast carcinogenesis. It has a barrier effect towards excessive cellular growth, acting as the regulator of tumour initiation and progression. Later in carcinogenesis, acquisition of the senescence associated secretory phenotype may instead promote tumour progression by stimulating growth and transformation in adjacent cells. This two-edge role of senescence in cancer directs more investigations into the effects of the senescence pathway in the development of malignancy. This review presents the current evidence on the roles of senescence molecular pathways in breast cancer and its progression.

Cardiac signal extraction in patients with Implantable Cardioverter Defibrillators

According to the guidelines the indication for Implantable Cardioverter Defibrillator (ICD) implantation is based on the ejection fraction. However, only a fraction of patients with implanted ICD shows live threatening arrhythmic events followed by adequate shocks. For this reason, further research is needed to find a more sensitive risk stratificator for patients prone to ventricular tachycardia or fibrillation. Unfortunately, standard prospective studies are time consuming. An alternative approach is to perform retrospective studies on patients with already implanted ICDs. So far, an implanted ICD is an exclusion criterion for Magnetic Field Imaging (MFI) studies. To overcome this problem several Blind Source Separation (BSS) algorithms have been tested to find out whether it is possible to separate the disturbances from the cardiac signals, in spite of the extreme difference in amplitude. Not all the methods are able to separate cardiac signal and disturbances. Temporal Decorrelation source Separation (TDSEP) is found to be superior both from a separation and performing point of view. For the first time it is possible to extract cardiac signals from measurements disturbed by an ICD, offering the possibility for a QRS-fragmentation analysis in patients with already implanted ICDs.

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.

Real-time fetal heart monitoring in biomagnetic measurements using adaptive real-time ICA

Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the mother should be monitored continuously to provide an important indication of current well-being. Due to physical constraints of an fMEG system, it is not possible to use clinically established heart monitors for this purpose. Considering this constraint, we developed a real-time heart monitoring system for biomagnetic measurements and showed its reliability and applicability in research and for clinical examinations. The developed system consists of real-time access to fMEG data, an algorithm based on Independent Component Analysis (ICA), and a graphical user interface (GUI). The algorithm extracts the current fetal and maternal heart signal from a noisy and artifact-contaminated data stream in real-time and is able to adapt automatically to continuously varying environmental parameters. This algorithm has been named Adaptive Real-time ICA (ARICA) and is applicable to real-time artifact removal as well as to related blind signal separation problems.

A robust fetal ECG detection method for abdominal recordings

In this paper, we propose a new method for FECG detection in abdominal recordings. The method consists of a sequential analysis approach, in which the a priori information about the interference signals is used for the detection of the FECG. Our method is evaluated on a set of 20 abdominal recordings from pregnant women with different gestational ages. Its performance in terms of fetal heart rate (FHR) detection success is compared with that of independent component analysis (ICA). The results show that our sequential estimation method outperforms ICA with a FHR detection rate of 85% versus 60% of ICA. The superior performance of our method is especially evident in recordings with a low signal-to-noise ratio (SNR). This indicates that our method is more robust than ICA for FECG detection.

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.

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

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.

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.

Reconstruction of Fetal Cardiac Vectors From Multichannel fMCG Data Using Recursively Applied and Projected Multiple Signal Classification

Previous attempts at unequivocal specification of signal strength in fetal magnetocardiographic (fMCG) recordings have used an equivalent current dipole (ECD) to estimate the cardiac vector at the peak of the averaged QRS complex. However, even though the magnitude of fetal cardiac currents are anticipated to be relatively stable, ECD-based estimates of signal strength show substantial and unrealistic variation when comparing results from different time windows of the same recording session. The present study highlights the limitations of the ECD model, and proposes a new methodology for fetal cardiac source reconstruction. The proposed strategy relies on recursive subspace projections to estimate multiple dipoles that account for the distributed myocardial currents. The dipoles are reconstructed from spatio-temporal fMCG data, and are subsequently used to derive estimators of the cardiac vector over the entire QRS. The new method is evaluated with respect to simulated data derived from a model of ventricular depolarization, which was designed to account for the complexity of the fetal cardiac source configuration on the QRS interval. The results show that the present methodology overcomes the drawbacks of conventional ECD fitting, by providing robust estimators of the cardiac vector. Additional evaluation with real fMCG data show fetal cardiac vectors whose morphology closely resembles that obtained in adult MCG.

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.

Noise reduction in magnetocardiography by singular value decomposition and independent component analysis

In the routine recording of magnetocardiograms (MCGs), it is necessary to underline the problem of noise cancellation. Source separation has often been suggested to solve this problem. In this paper, blind source separation (BSS), by means of singular value decomposition (SVD) and independent component analysis (ICA), was used for noise reduction in MCG data to improve the signal to noise ratio. Special techniques, based on statistical parameters, for identifying noise and disturbances, have been introduced to automatically eliminate noise-related and disturbance-related components before reconstructing cleaned data sets. The results show that ICA and SVD can detect and remove a variety of noise and artefact sources from MCG data, as well as from stress MCG.

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.

Beat-to-beat estimate of fetal cardiac time intervals using magnetocardiography: longitudinal charts of normality ranges and individual trends

BACKGROUND

Fetal magnetocardiography (fMCG) records fetal cardiac electro-physiological activity during the second half of gestation. We aimed at assessing normality values, related variability, and trends of fetal cardiac time intervals (fCTI) evaluated longitudinally from beat-to-beat fMCG analysis in uncomplicated pregnancies.

MATERIALS AND METHODS

The fMCG were recorded with multi-channel system in shielded room. FCTI were estimated on more than 2600 fetal cardiac cycles from 51 fMCG data sets of uncomplicated pregnancies. Independent component analysis (ICA) allowed reconstructing reliable fetal signals for beat-to-beat identification of fCTI (RR, P wave, PQ, PR, QT, QTc, QRS, ST, and T wave); intra-individual variability analysis and trends were calculated; reference longitudinal charts accounted for intra- and inter-individual variations and were compared with figures estimated on averaged signals.

RESULTS

For each data set, fCTI were calculated beat-to-beat on rhythm strips of more than 50 beats (95% overall detection rate). FCTI values, variability, and trends were in good agreement with available reference figures; intervals related to P and T waves were, respectively, underestimated and overestimated with respect to those estimated on averaged signals or obtained by other research groups. Errors were reduced and individual trends could be drawn.

CONCLUSIONS

ICA permitted the reconstruction of reliable time course of fetal cardiac signals and the beat-to-beat calculation of time intervals, and normality ranges, with smaller errors with respect to previous studies. The retrieval of fetal traces with clear morphology and the longitudinal character of the study allowed estimating individual trends and beat-to-beat characterization, impossible with cross-sectional studies on averaged beats.

Optimal filter design for shielded and unshielded ambient noise reduction in fetal magnetocardiography

The greatest impediment to extracting high-quality fetal signals from fetal magnetocardiography (fMCG) is environmental magnetic noise, which may have peak-to-peak intensity comparable to fetal QRS amplitude. Being an unstructured Gaussian signal with large disturbances at specific frequencies, ambient field noise can be reduced with hardware-based approaches and/or with software algorithms that digitally filter magnetocardiographic recordings. At present, no systematic evaluation of filters' performances on shielded and unshielded fMCG is available. We designed high-pass and low-pass Chebychev II-type filters with zero-phase and stable impulse response; the most commonly used band-pass filters were implemented combining high-pass and low-pass filters. The achieved ambient noise reduction in shielded and unshielded recordings was quantified, and the corresponding signal-to-noise ratio (SNR) and signal-to-distortion ratio (SDR) of the retrieved fetal signals was evaluated. The study regarded 66 fMCG datasets at different gestational ages (22-37 weeks). Since the spectral structures of shielded and unshielded magnetic noise were very similar, we concluded that the same filter setting might be applied to both conditions. Band-pass filters (1.0-100 Hz) and (2.0-100 Hz) provided the best combinations of fetal signal detection rates, SNR and SDR; however, the former should be preferred in the case of arrhythmic fetuses, which might present spectral components below 2 Hz.

Artifact reduction in magnetogastrography using fast independent component analysis

The analysis of magnetogastrographic (MGG) signals has been limited to epochs of data with limited interference from extraneous signal components that are often present and may even dominate MGG data. Such artifacts can be of both biological (cardiac, intestinal and muscular activities, motion artifacts, etc) and non-biological (environmental noise) origin. Conventional methods-such as Butterworth and Tchebyshev filters-can be of great use, but there are many disadvantages associated with them as well as with other typical filtering methods because a large amount of useful biological information can be lost, and there are many trade-offs between various filtering methods. Moreover, conventional filtering cannot always fully address the physicality of the signal-processing problem in terms of extracting specific signals due to particular biological sources of interest such as the stomach, heart and bowel. In this paper, we demonstrate the use of fast independent component analysis (FICA) for the removal of both biological and non-biological artifacts from multi-channel MGG recordings acquired using a superconducting quantum intereference device (SQUID) magnetometer. Specifically, we show that the signal of gastric electrical control activity (ECA) can be isolated from SQUID data as an independent component even in the presence of severe motion, cardiac and respiratory artifacts. The accuracy of the method is analyzed by comparing FICA-extracted versus electrode-measured respiratory signals. It is concluded that, with this method, reliable results may be obtained for a wide array of magnetic recording scenarios.

A method for the automatic reconstruction of fetal cardiac signals from magnetocardiographic recordings

Fetal magnetocardiography (fMCG) allows monitoring the fetal heart function through algorithms able to retrieve the fetal cardiac signal, but no standardized automatic model has become available so far. In this paper, we describe an automatic method that restores the fetal cardiac trace from fMCG recordings by means of a weighted summation of fetal components separated with independent component analysis (ICA) and identified through dedicated algorithms that analyse the frequency content and temporal structure of each source signal. Multichannel fMCG datasets of 66 healthy and 4 arrhythmic fetuses were used to validate the automatic method with respect to a classical procedure requiring the manual classification of fetal components by an expert investigator. ICA was run with input clusters of different dimensions to simulate various MCG systems. Detection rates, true negative and false positive component categorization, QRS amplitude, standard deviation and signal-to-noise ratio of reconstructed fetal signals, and real and per cent QRS differences between paired fetal traces retrieved automatically and manually were calculated to quantify the performances of the automatic method. Its robustness and reliability, particularly evident with the use of large input clusters, might increase the diagnostic role of fMCG during the prenatal period.

Fetal source extraction from magnetocardiographic recordings by dependent component analysis

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

Fetal magnetocardiographic mapping using independent component analysis

Fetal magnetocardiography (fMCG) is the only noninvasive technique allowing effective assessment of fetal cardiac electrical activity during the prenatal period. The reconstruction of reliable magnetic field mapping associated with fetal heart activity would allow three-dimensional source localization. The efficiency of independent component analysis (ICA) in restoring reliable fetal traces from multichannel fMCG has already been demonstrated. In this paper, we describe a method of reconstructing a complete set of fetal signals hidden in multichannel fMCG preserving their correct spatial distribution, waveform, polarity and amplitude. Fetal independent components, retrieved with an ICA algorithm (FastICA), were interpolated (fICI method) using information gathered during FastICA iterations. The restored fetal signals were used to reconstruct accurate magnetic mapping for every millisecond during the average beat. The procedure was validated on fMCG recorded from the 22nd gestational week onward with a multichannel MCG system working in a shielded room. The interpolated traces were compared with those obtained with a standard technique, and the consistency of fetal mapping was checked evaluating source localizations relative to fetal echocardiographic information. Good magnetic field distributions during the P-QRS-T waves were attained with fICI for all gestational periods; their reliability was confirmed by three-dimensional source localizations.

Automatic detection of cardiac waves on fetal magnetocardiographic signals

Fetal magnetocardiography (fMCG) provides fetal cardiac traces useful for the prenatal monitoring of fetal heart function. In this paper, we describe an analytical model (ACWD) for the automatic detection of cardiac waves boundaries that works on fetal signals reconstructed from fMCG by means of independent component analysis. ACWD was validated for 45 healthy and 4 arrhythmic fetuses ranging from 22 to 37 weeks; ACWD outcomes were compared with the estimates of three independent investigators. Descriptive statistics were used to assess correspondence between the outcomes of the automatic and manual approaches. The parametric two-tailed Pearson correlation test (alpha=0.01) was employed to quantify, by means of the coefficients of determination, the amount of common variation between the sequences of intervals quantified automatically and manually. ACWD performances on short and long rhythm strips were investigated. ACWD demonstrated to be a robust tool providing dependable estimates of cardiac intervals and their variability during the third gestational trimester also in case of fetal arrhythmias. SNR and stability of fetal traces were the factors limiting ACWD performances. ACWD computation time, which was approximately 1:600 with respect to the manual procedure, was comparable with the time required for fCTI estimation on averaged beats.

Simultaneous monitoring of separate fetal magnetocardiographic signals in twin pregnancy

Fetal magnetocardiography (fMCG) allows the non-invasive recording of fetal cardiac electrical activity with increasing efficacy as gestation progresses. Many reports on the successful extraction of reliable fetal magnetocardiographic traces in singleton pregnancies exist in the literature, whereas there is only one report on the reconstruction of averaged fetal cardiac signals obtained in a twin pregnancy with the use of a double sensor array system. In this paper, we aimed at assessing the effectiveness of an ICA-based procedure to reconstruct the time course of fetal cardiac signals recorded with a single-shot multi-channel fMCG device in an uncomplicated twin pregnancy at 27 weeks. The evaluation of heart rate and beats synchronicity permitted the differentiation of fetal components; the quality of reconstructed fetal signals allowed visual inspection on single cycles and the simultaneous monitoring of separate fetal heart rate patterns. The proposed technique might be applied in twin pregnancies not only to characterize fetal arrhythmias, but also in all cases of discordant fetal growth, either in the case of intra-uterine growth retardation affecting one fetus, or in the case of twin-twin transfusion syndrome, a life-threatening condition where both fetuses are at risk of heart failure.

Multichannel mapping of fetal magnetocardiogram in an unshielded hospital setting

OBJECTIVES

To evaluate the feasibility of unshielded in-hospital multichannel mapping of fetal magnetocardiogram (FMCG), with a 36-channel system for standard adult magnetocardiographic (MCG) recordings, and its reliability according to the recommended standards for FMCG.

METHODS

FMCG was ambulatory mapped with a 36-channel MCG system, in six normal pregnancies at different gestational ages. MCG analysis included adaptive digital filtering of 50 Hz, signal averaging, reconstruction of magnetic field distribution (MFD) and source localization. Fixed Point Independent Component Analysis algorithm (FastICA) was used to reconstruct the FMCG, separating them from maternal contamination and noise.

RESULTS

The quality of FMCG recorded after the 32nd gestational week and reconstructed with FastICA was close to FMCG obtained in shielded rooms, and good enough to measure cardiac intervals and heart rate variability parameters. In two cases, reconstruction of the MFD during the QRS allowed three-dimensional localization of ventricular sources.

CONCLUSIONS

A first demonstration has been given that multichannel mapping of FMCG can be performed in unshielded clinical environments, with resolution good enough for contactless assessment of fetal cardiac electrophysiology. FastICA processing on unshielded FMCG, recorded after the 32nd week, provided beat-to-beat analysis and heart rate variability assessment. Further work is needed to improve signal reconstruction in early pregnancy.

Characterization of Fetal Arrhythmias by Means of Fetal Magnetocardiography in Three Cases of Difficult Ultrasonographic Imaging

Characterization of ultrasound detected fetal arrhythmias is generally performed by means of M-mode and pulsed Doppler echocardiography (fECHO), sonographic techniques that allow only indirect and approximate reconstruction of the true electrophysiological events that occur in the fetal heart. Several studies demonstrated the ability of fetal magnetocardiography (fMCG) to identify fetal arrhythmias. We report on three women, studied after the 32nd gestational week, who were referred for fMCG because of unsatisfying fetal cardiac visualization with fECHO due to maternal obesity, fetus in constant dorsal position hiding the fetal heart, intrauterine growth retardation, and oligohydramnios. Minor pericardial effusion was present in the third patient and digoxin therapy was given. FMCG were recorded with a 77-channel MCG system working in a shielded room. Independent Component Analysis (FastICA algorithm) was used to reconstruct fetal signals. The good quality of the retrieved fetal signals allowed real-time detection of arrhythmias and their classification as supraventricular extrasystoles (SVE), with/without aberrant ventricular conduction and/or atrioventricular block. The time course of the fetal cardiac rhythm was reconstructed for the entire recording duration; hence, fetal heart rate variability could be studied in time and frequency. Since isolated extrasystoles may progress to more hazardous supraventricular tachycardias, the noninvasive antenatal characterization of, even transient, fetal arrhythmias and their monitoring during pregnancy can be of great clinical impact.