Morphology extraction of fetal electrocardiogram by slow-fast LSTM network

Zhou, Ziqun; Huang, Kejie; Qiu, Yue; Shen, Haibin; Ming, Zhaoyan

doi:10.1016/j.bspc.2021.102664

Cited by 14 publications

(7 citation statements)

References 44 publications

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“…Objective comparison of results is challenging due to the different methodology applied in each study. The authors use different types of signals (real or simulated 30 , 31 ). Moreover, these signals come from different databases, e.g.…”

Section: Discussionmentioning

confidence: 99%

Nature inspired method for noninvasive fetal ECG extraction

Raj

Brablík

Kahánková

et al. 2022

Sci Rep

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This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women’s body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1–14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions.

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Section: Discussionmentioning

confidence: 99%

Nature inspired method for noninvasive fetal ECG extraction

Raj

Brablík

Kahánková

et al. 2022

Sci Rep

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“…Finally, we compared the results with those achieved by other authors in the last 2 years, see www.nature.com/scientificreports/ use different types of signals (real or simulated 30,31 ). Moreover, these signals come from different databases, e.g.…”

Section: Discussionmentioning

confidence: 99%

Nature Inspired Method for Noninvasive Fetal ECG Extraction

Raj

Brablík

Kahánková

et al. 2022

Preprint

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This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal Electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women’s body, especially maternal electro-cardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with Sequential Analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%.

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“…As a result, extracting the FECG from an abdominal mixed signal is susceptible to influence from the strong MECG component [ 9 ]. Moreover, the AECG recording is usually distorted by various noises, such as maternal electromyogram (EMG), power line interference, baseline drift and white noise [ 10 ]. The above interferences would severely disrupt the extraction of the FHR and FECG morphology.…”

Section: Introductionmentioning

confidence: 99%

Morphology extraction of fetal ECG using temporal CNN-based nonlinear adaptive noise cancelling

Cao

Xiao²,

Gong³

et al. 2022

PLoS ONE

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Objectives Noninvasive fetal electrocardiography (FECG) offers many advantages over alternative fetal monitoring techniques in evaluating fetal health conditions. However, it is difficult to extract a clean FECG signal with morphological features from an abdominal ECG recorded at the maternal abdomen; the signal is usually contaminated by the maternal ECG and various noises. The aim of the work is to extract an FECG signal that preserves the morphological features from the mother’s abdominal ECG recording, which allows for accurately estimating the fetal heart rate (FHR) and analyzing the waveforms of the fetal ECG. Methods We propose a novel nonlinear adaptive noise cancelling framework (ANC) based on a temporal convolutional neural network (CNN) to effectively extract fetal ECG signals from mothers’ abdominal ECG recordings. The proposed framework consists of a two-stage network, using the ANC architecture; one network is for the maternal ECG component elimination and the other is for the residual noise component removal of the extracted fetal ECG signal. Then, JADE (one of the blind source separation algorithms) is applied as a postprocessing step to produce a clean fetal ECG signal. Results Synthetic ECG data (FECGSYNDB) and clinical ECG data (NIFECGDB, PCDB) are used to evaluate the extraction performance of the proposed framework. The statistical and visual results demonstrate that our method outperforms the other state-of-the-art algorithms in the literature. Specifically, on the FECGSYNDB, the mean squared error (MSE), signal-to-noise ratio (SNR), correlation coefficient (R) and F1-score of our method are 0.16, 7.94, 0.95 and 98.89%, respectively. The F1-score on the NIFECGDB reaches 98.62%. The value of the F1-score on the PCDB is 98.62%. Conclusion As opposed to the existing algorithms being restricted to fetal QRS complex detection, the proposed framework can preserve the morphological features of the extracted fetal ECG signal well, which could support medical diagnoses based on the morphology of the fetal ECG signal.

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Morphology extraction of fetal electrocardiogram by slow-fast LSTM network

Cited by 14 publications

References 44 publications

Nature inspired method for noninvasive fetal ECG extraction

Nature inspired method for noninvasive fetal ECG extraction

Nature Inspired Method for Noninvasive Fetal ECG Extraction

Morphology extraction of fetal ECG using temporal CNN-based nonlinear adaptive noise cancelling

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