Independent component analysis: fetal signal reconstruction from magnetocardiographic recordings

Frequency-Based Maternal Electrocardiogram Attenuation for Fetal Electrocardiogram Analysis

Fetal electrocardiogram (ECG) waveform analysis along with cardiac time intervals (CTIs) measurements are critical for the management of high-risk pregnancies. Currently, there is no system that can consistently and accurately measure fetal ECG. In this work, we present a new automatic approach to attenuate the maternal ECG in the frequency domain and enhance it with measurable CTIs. First, the coherent components between the maternal ECG and abdominal ECG were identified and subtracted from the latter in the frequency domain. The residual was then converted into the time domain using the inverse Fourier transform to yield the fetal ECG. This process was improved by averaging multiple beats. Two fetal cardiologists, blinded to the method, assessed the quality of fetal ECG based on a grading system and measured the CTIs. We evaluated the fetal ECG quality of our method and time-based methods using one synthetic dataset, one human dataset available in the public domain, and 37 clinical datasets. Among the 37 datasets analyzed, the mean average (± standard deviation) grade was 3.49 ± 1.22 for our method vs. 2.64 ± 1.26 for adaptive interference cancellation (p-value < 0.001), thus showing the frequency-based fetal ECG extraction was the superior method, as assessed from our clinicians' perspectives. This method has the potential for use in clinical settings.

Detection of fetal arrhythmia by adaptive single channel electrocardiogram extraction

Fetal arrhythmia, the abnormal heartbeat of a fetus is broadly classified as tachy arrhythmia (too fast > 160 beats/min) and brady arrhythmia (too slow < 120 beats/min). Detection of this irregular heart beat rhythm of the fetus during pregnancy is still a challenging task for the clinicians. Heart rate detection through electrocardiography has always been accurate for identifying cardiac defect in humans. Adult ECG has achieved several developments in the modern medicine whereas noninvasive fetal ECG (FECG) continues to be a big challenge. Automatic detection of fetal heart rate is vital for monitoring the unborn infant during pregnancy. The non-invasive placement of electrodes over the abdomen region of pregnant women records the ECG signal of both mother and fetus. The arrhythmia affected FECG signals (n = 14) are processed from the physionet database. This raw ECG signal is preprocessed using a Savitzky-Golay filter and symlet wavelet transform to remove the basic noises. Adaptive recursive least square filter is preferably chosen for extracting the FECG, using mother's thorax ECG as a reference. An accurate PQRST wave-shape of the FECG is required for the proper diagnosis of fetal cardiac defects. Using a single channel abdominal ECG signal, the proposed work generates extracted fetal ECG and an automated visual display of fetal heart rate. The presence of arrhythmia and fetal distress can be analyzed through fetal heart rate display and abnormal conductivity of PQRST wave respectively. We have analyzed fetal arrhythmias through ECG extraction and the same was compared with the echocardiograph results given by pediatric cardiologist. This study helps to identify the fetal distress at early gestational age that helps the obstetricians to make quick decisions before or immediately after delivery.

Verification of fetal evoked response by magnetic dipole fitting

BACKGROUND

Investigation of fetal evoked response to auditory or visual stimuli is an important means of understanding the developmental stages and potential problems in prenatal life. It is, however, not without certain imperfections. The biggest challenge with fetal evoked response is its low signal to noise ratio. Under noisy conditions, the detected fetal evoked response should, therefore, be further investigated to confirm that the source of the signal is from fetal brain and is not related to random noise. Existing methods for verification are: (1) visual inspection of magnetic field maps, which requires user intervention and expert knowledge which can be highly subjective; (2) simultaneous ultrasound measurement, which is expensive and technically difficult to manage; and (3) equivalent current dipole fitting, which requires knowledge of the orientation of fetal head and its dimensions that may not be available at all times.

OBJECTIVE

To verify that the detected fetal evoked response signal is originating from the fetal head by using an objective and feasible method that employs magnetic dipole fitting to fetal evoked response.

STUDY DESIGN

From raw fetal magnetoencephalography data, the cardiac interference was removed by frequency dependent subtraction. After averaging over stimulus triggers, the resulting signal was taken as the candidate fetal evoked response. The fetal evoked response was investigated for the highest peak in between 0.2-0.5 s, which is the expected latency of the response to the stimulus. The magnetic field at this highest peak was used for magnetic dipole fitting. The validation of peak was based on the closeness of the magnetic dipole fit to vicinity of fetal head location determined by ultrasound and the anatomically reasonable distance from the fetal heart. The methodology was first tested on a sample neonatal data before application to fetal data.

RESULTS

The results of neonatal application confirmed that the source localization by magnetic dipole fitting for the brain produced meaningful results. When applied to fetal data, auditory and visual evoked response was detected in 27 of the 38 recordings. This implied that with our verification method, fetal evoked responses were detected in 71% of fetuses.

CONCLUSION

Detection rate of the evoked responses were similar to earlier reports where subjective visual inspection or simultaneous ultrasound measurement were used. Our method using magnetic dipole fitting for verification is more feasible and objective compared to the earlier methods.

Separation of Physiological Signals Using Minimum Norm Projection Operators

OBJECTIVE

This paper presents the development of a fast and robust method which can be applied to multichannel physiologic signals for the purpose of either removing a selected interfering signal or separating signals that arise from temporally correlated and spatially distributed signals such as maternal or fetal cardiac waveform recordings.

METHODS

Projection operators based upon both the weighted and un-weighted minimum norm equations are presented. The weighted formulation uses models based on signal covariance and the un-weighted formulation requires that a statistical model be built using time-locked averaging.

RESULTS

We present examples that demonstrate the utility of our projection operators when applied to maternal and fetal magneto-cardiograms. In addition, we demonstrate the ability to separate fetal breathing signals from both maternal and fetal cardiac signals.

CONCLUSION

The method is effective, robust, fast, and does not require significant input from a user.

SIGNIFICANCE

Although we demonstrate the utility of our projection operators applied to biomagnetic signals, the method can easily be adapted to other applications were the goal is to either separate or suppress selected signal components.

Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics

Comprehensive gas chromatography-mass spectrometry (GC×GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC-MS chromatograms in an automated manner. We studied the performance of ICA-OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GC×GC-MS. The quantification by ICA-OSD was compared with a supervised quantification by selective ions, and most of the R(2) coefficients of determination were in good agreement (R(2)>0.90) while up to 24 cases exhibited an excellent linear relation (R(2)>0.95). We concluded that ICA-OSD can be used to resolve co-eluted compounds in GC×GC-MS.

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.

Baseline drift removal and denoising of MCG data using EEMD: role of noise amplitude and the thresholding effect

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).

Classification of independent components of EEG into multiple artifact classes

In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them.

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.

Linear and nonlinear measures of fetal heart rate patterns evaluated on very short fetal magnetocardiograms

We analyzed the effectiveness of linear short- and long-term variability time domain parameters, an index of sympatho-vagal balance (SDNN/RMSSD) and entropy in differentiating fetal heart rate patterns (fHRPs) on the fetal heart rate (fHR) series of 5, 3 and 2 min duration reconstructed from 46 fetal magnetocardiograms. Gestational age (GA) varied from 21 to 38 weeks. FHRPs were classified based on the fHR standard deviation. In sleep states, we observed that vagal influence increased with GA, and entropy significantly increased (decreased) with GA (SDNN/RMSSD), demonstrating that a prevalence of vagal activity with autonomous nervous system maturation may be associated with increased sleep state complexity. In active wakefulness, we observed a significant negative (positive) correlation of short-term (long-term) variability parameters with SDNN/RMSSD. ANOVA statistics demonstrated that long-term irregularity and standard deviation of normal-to-normal beat intervals (SDNN) best differentiated among fHRPs. Our results confirm that short- and long-term variability parameters are useful to differentiate between quiet and active states, and that entropy improves the characterization of sleep states. All measures differentiated fHRPs more effectively on very short HR series, as a result of the fMCG high temporal resolution and of the intrinsic timescales of the events that originate the different fHRPs.

Maternal MCG interference cancellation using splined independent component subtraction

Signal distortion is commonly observed when using independent component analysis (ICA) to remove maternal cardiac interference from the fetal magnetocardiogram. This can be seen even in the most conservative case where only the independent components dominated by maternal interference are subtracted from the raw signal, a procedure we refer to as independent component subtraction (ICS). Distortion occurs when the subspaces of the fetal and maternal signals have appreciable overlap. To overcome this problem, we employed splining to remove the fetal signal from the maternal source component. The maternal source components were downsampled and then interpolated to their original sampling rate using a cubic spline. A key aspect of the splining procedure is that the maternal QRS complexes are downsampled much less than the rest of the maternal signal so that they are not distorted, despite their higher bandwidth. The splined maternal source components were projected back onto the magnetic field measurement space and then subtracted from the raw signal. The method was evaluated using data from 24 subjects. We compared the results of conventional, i.e., unsplined, ICS with our method, splined ICS, using matched filtering as a reference. Correlation and subjective assessment of the P-wave and QRS complex were used to assess the performance. Using ICS, we found that the P-wave was adversely affected in 7 of 24 (29%) subjects, all having correlations less than 0.8. Splined ICS showed negligible distortion and improved the signal fidelity to some extent in all subjects. We also demonstrated that maternal T-wave interference could be problematic when the fetal and maternal heartbeats were synchronous. In these instances, splined ICS was more effective than matched filtering.

Independent component analysis of normal and abnormal rhythm in twin pregnancies

We investigated the utility of ICA for evaluation of fetal rhythm in five uncomplicated twin pregnancies and in five twin pregnancies complicated by fetal arrhythmia. Using objective and subjective criteria, we sought to determine how the signal-to-noise ratio, signal fidelity and interference rejection are affected when synthesizing the fetal signal using all the signal-containing ICA components (rank-p ICA) versus using the single dominant component (rank-1 ICA). The signal of each fetus was most commonly distributed over 1 or 2 ICA components, as previously observed in studies of singleton pregnancies; however, in 8 of 26 (31%) cases the signal of each fetus was distributed over 3, 4 or even 5 ICA components. Rank-1 ICA provided the highest SNR and interference rejection, but at the cost of reduced signal fidelity. Our results corroborate that in twin pregnancies, including twin pregnancies complicated by fetal arrhythmia, rank-1 ICA is very effective in isolating the QRS complexes of each fetus; however, it has some limitations when used for fetal rhythm evaluation due to signal distortion. Occasionally, rank-1 ICA completely separates the P-wave and the T-wave from the QRS complex, thus requiring the mixing of several ICA components to achieve acceptable signal fidelity.

Influence of gestational age on the effectiveness of spatial and temporal methods for the reconstruction of the fetal magnetocardiogram

Fetal magnetocardiography (fMCG) has been shown to augment fetal ultrasound evaluation for high-risk conditions, but the clinical utility of fMCG depends on the reliability of the cardiac traces reconstructed. We performed a methodological study to examine the influence of gestational age on the properties of the fetal magnetocardiograms extracted with two methods of signal reconstruction: the template matching technique (TMT), which extracts the maternal components from the signal using only temporal information, and independent component analysis (ICA), which separates the fetal signals by using information on the spatial distribution of the mixed source signals in addition to higher order temporal statistics. Efficiency and accuracy were evaluated in terms of fetal beat detection, signal characteristics, and duration of cardiac time intervals (CTIs) on the averaged traces. ICA outperformed TMT with regard to beat detection and signal-to-noise ratio. The timing of the heartbeats and the duration of the CTIs were essentially the same, whereas some alterations in signal morphology were observed in the ICA traces. We conclude that ICA may be useful in early gestation when the signals are noisy, while TMT may be preferred when accurate beat morphology is required for diagnostic purposes.

Integrated approach for fetal QRS detection

Fetal magnetocardiography provides reliable signals of the fetal heart dynamics with high temporal resolution that can be used in a clinical setting. We present a robust Hilbert transform method for extraction of the fetal heart rate. Our method may be applied to signals derived from a single channel or an array of channels. In the case of multichannel data, the channels can be combined to improve signal-to-noise ratio for the extraction of fetal heart data. The method is inherently insensitive to fetal position or movement and, in addition, can be automated. We demonstrate that the determination of R-wave timing is relatively insensitive to waveform morphology. The method can also be applied if the data were preprocessed by independent component analysis (ICA). We compared the Hilbert method, ICA, ICA + Hilbert, and raw signals and found that the Hilbert method gave the best overall performance. We demonstrated that there were approximately 171 errors in 46,789 fetal heart beats.

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 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.

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.

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.

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

M-mode and pulsed Doppler echocardiography, cardiotocography and transabdominal fetal ECG are available in clinical practice to monitor fetal cardiac activity during advancing gestation, but none of these methods allows the direct measurement of morphological and temporal parameters for fetal rhythm assessment. Fetal magnetocardiograms (fMCGs) are noninvasive recordings of magnetic field variations associated with electrical activity of the fetal heart obtained with superconducting sensors positioned over the maternal abdomen inside a shielded room. Because of maternal cardiac activity, fMCGs are contaminated by maternal components that need to be eliminated to reconstruct fetal cardiac traces. The aim of the present work was to use two methods working in the time domain, an independent component analysis algorithm (FastICA) and an adaptive maternal beat subtraction technique (AMBS), for the retrieval of fetal cardiac signals from fMCGs. Detection rates of both methods were calculated, and FastICA and AMBS performances were compared in the context of clinical applications by estimating several temporal and morphological characteristics of the retrieved fetal traces, such as the shape and duration P-QRS-T waves, arrhythmic beat detection and classification, and noise reduction. Quantitative and qualitative comparison produced figures that always suggested that FastICA was superior to AMBS from the perspective of clinical use of the recovered fetal signals.