Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease

INTRODUCTION: This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.MATERIAL AND METHODS: An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance.RESULTS: Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. A small negative trend between gestational age and accuracy was found.CONCLUSIONS: The proposed method combining recent advances in obtaining non-invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography-based screening complementary to the standard ultrasound-based screening. More research is required to improve performance and determine the benefits to clinical practice.</p

Electrophysiological monitoring of uterine contractions

\u3cp\u3eA signal processing arrangement 130, a monitoring system 100, a signal processing method, a monitoring method of monitoring uterine contractions of a pregnant woman, and a computer program product are provided. The signal processing arrangement 130 receives an electrophysiological signal 116 representing uterine muscle activity of a pregnant woman at an input 132. A filter 136 generates a filtered electrohysterogram signal from the electrophysiological signal 116. The filter 136 allows the passage of spectral components between 0 and 3 Hz. A window function applicator 138 applies a window function to the filtered electrohysterogram signal to obtain an output waveform 146. The window function defines that samples of a time interval preceding the application of the window function need to be used The output waveform 146 simulates output data of tocodynamometer or an intra-uterine pressure catheter. The output waveform 146 is provided at an output 144 of the signal processing arrangement.\u3c/p\u3

ECG signal processing

\u3cp\u3eA system extracts an ECG signal from a composite signal (308) representing an electric measurement of a living subject. Identification means (304) identify a plurality of temporal segments (309) of the composite signal corresponding to a plurality of predetermined segments (202,204,206) of an ECG complex (208). A template generator (306) generates a plurality of respective template segments (310) corresponding to the respective predetermined segments (202,204,206), wherein a respective template segment is based on a plurality of the corresponding identified temporal segments, the template segments representing extracted portions of the ECG signal. A signal generator (312) combines a plurality of the template segments (310) to obtain an extracted ECG signal (314). The composite signal may comprise a maternal ECG signal and a fetal ECG signal, or the ECG signal and an EMG signal.\u3c/p\u3

Probabilistic source separation for robust fetal electrocardiography

Blind source separation (BSS) techniques are widely used to extract signals of interest from a mixture with other signals, such as extracting fetal electrocardiogram (ECG) signals from noninvasive recordings on the maternal abdomen. These BSS techniques, however, typically lack possibilities to incorporate any prior knowledge on the mixing of the source signals. Particularly for fetal ECG signals, knowledge on the mixing is available based on the origin and propagation properties of these signals. In this paper, a novel source separationmethod is developed that combines the strengths and accuracy of BSS techniques with the robustness of an underlying physiological model of the fetal ECG.The method is developed within a probabilistic framework and yields an iterative convergence of the separation matrix towards a maximum a posteriori estimation, where in each iteration the latest estimate of the separation matrix is corrected towards a tradeoff between the BSS technique and the physiological model. The method is evaluated by comparing its performance with that of FastICA on both simulated and real multichannel fetal ECG recordings, demonstrating that the developed method outperforms FastICA in extracting the fetal ECG source signals

Fetal monitoring

\u3cp\u3eA system for monitoring a fetus during gestation comprises an input for receiving a plurality of electric signals measured on a surface of a maternal body; and means for providing a fetal electrocardiogram based on the received electric signals and based on an orientation of the fetus, wherein the fetal electrocardiogram represents a projection of a fetal cardiac potential vector according to a predetermined projection direction that is fixed with respect to the fetus. The fetal vector electrocardiogram is projected according to the projection direction. An at least partial representation of a fetal vector electrocardiogram is provided in dependence on the plurality of electric signals and indicative of a time path of an electrical field vector generated by a fetal heart of the fetus.\u3c/p\u3

End-to-end trained encoder–decoder convolutional neural network for fetal electrocardiogram signal denoising

Objective: Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. However, the low signal-to-noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Quality improvement of the fetal ECG is of great importance for providing accurate information to enable support in medical decision-making. In this paper we propose the use of artificial intelligence for the task of one-channel fetal ECG enhancement as a post-processing step after maternal ECG suppression. Approach: We propose a deep fully convolutional encoder–decoder framework, learning end-to-end mappings from noise-contaminated fetal ECGs to clean ones. Symmetric skip-layer connections are used between corresponding convolutional and transposed convolutional layers to help recover the signal details. Main results: Experiments on synthetic data show an average improvement of 7.5 dB in the signal-to-noise ratio (SNR) for input SNRs in the range of  −15 to 15 dB. Application of the method with real signals and subsequent ECG interval analysis demonstrates a root mean square error of 9.9 and 14 ms for the PR and QT intervals, respectively, when compared with simultaneous scalp measurements. The proposed network can achieve substantial noise removal on both synthetic and real data. In cases of highly noise-contaminated signals some morphological features might be unreliably reconstructed. Significance: The presented method has the advantage of preserving individual variations in pulse shape and beat-to-beat intervals. Moreover, no prior knowledge on the power spectra of the noise or the pulse locations is required

Deep convolutional encoder-decoder framework for fetal ECG signal denoising

Non-invasive fetal electrocardiography has the potential of providing vital information for evaluating the health status of the fetus. However, the low signal to noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Residual noise in the fetal ECG, after the maternal ECG is suppressed, is often non-stationary, complex and has spectral overlap with the fetal ECG. We present a deep fully convolutional encoder-decoder framework, for removing the residual noise from single-channel fetal ECG. The method was tested in a broad simulated fetal ECG dataset with varying amount of noise. The results demonstrate that after the denoising there was an average increase in the correlation coefficient between the corrupted signals and the original ones from 0.6 to 0.8. Moreover, the suggested framework successfully handled different levels of noises in a single model. The network was further tested on real signals showing substantial noise removal performance, thus providing a promising approach for fetal ECG signal denoising. The presented method is able to significantly improve the quality of the extracted fetal ECG signals, having the advantage of preserving beat-to-beat morphological variations

Enhancement of low-quality fetal electrocardiogram based on time-sequenced adaptive filtering

\u3cp\u3eExtraction of a clean fetal electrocardiogram (ECG) from non-invasive abdominal recordings is one of the biggest challenges in fetal monitoring. An ECG allows for the interpretation of the electrical heart activity beyond the heart rate and heart rate variability. However, the low signal quality of the fetal ECG hinders the morphological analysis of its waveform in clinical practice. The time-sequenced adaptive filter has been proposed for performing optimal time-varying filtering of non-stationary signals having a recurring statistical character. In our study, the time-sequenced adaptive filter is applied to enhance the quality of multichannel fetal ECG after the maternal ECG is removed. To improve the performance of the filter in cases of low signal-to-noise ratio (SNR), we enhance the ECG reference signals by averaging consecutive ECG complexes. The performance of the proposed augmented time-sequenced adaptive filter is evaluated in both synthetic and real data from PhysioNet. This evaluation shows that the suggested algorithm clearly outperforms other ECG enhancement methods, in terms of uncovering the ECG waveform, even in cases with very low SNR. With the presented method, quality of the fetal ECG morphology can be enhanced to the extent that the ECG might be fit for use in clinical diagnostics. [Figure not available: see fulltext.]\u3c/p\u3