Fetal Electrocardiogram Enhancement Research Articles

Morphology extraction of fetal electrocardiogram by slow-fast LSTM network

Fetal electrocardiogram (FECG) morphology plays an essential role in the early diagnosis of fetal health conditions. However, it is intractable to extract the clean morphology of FECG signals, which are usually contaminated by maternal ECG (MECG) and various noises. To extract the clean morphology of FECG signals from non-invasive abdominal ECG records, a high-performance and high-efficient two-stage slow-fast long short-term memory (SFLSTM) based architecture is proposed. The MECG elimination and the FECG enhancement are realized by the elaborately designed slow LSTM and fast LSTM to filter out the MECG and the residual noise components. Qualitative and quantitative experiments are conducted on the records from two public datasets. The experimental results reveal that our designed scheme achieves the best performance in kSQI, signal-to-noise ratio (SNR), and root mean square error (RMSE). The MECG elimination and the FECG enhancement improve SNR by 3.09 and 1.81 dB, respectively. The proposed fast LSTM reduces computation cost by approximately 50%, without any degradation in performance. Our method may leverage non-invasive FECG monitoring for the early detection of fetal heart diseases.

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.

Fetal Electrocardiogram Enhancement by Time-Sequenced Adaptive Filtering

An adaptive method for performing optimal time-varying filtering of nonstationary signals having a recurring statistical character, e.g., recurring pulses in noise, has been proposed. This method, called time-sequenced adaptive filtering, is applied to the enhancement of abdominally derived fetal electrocardiograms against background muscle noise. It is shown that substantial improvement in terms of signal distortion is obtained when time-sequenced filtering, rather than conventional time-invariant filtering, is employed. The method requires two or more abdominal channels containing correlated signal components, but uncorrelated muscle noise components. The location of the fetal pulses in time must be estimated in order to synchronize the filter's time-varying impulse response to the fetal cardiac cycle.