A survey on FECG extraction using neural network and adaptive filter

Kaleem, Abdullah Mohammed; Kokate, Rajendra D.

doi:10.1007/s00500-020-05447-w

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…Electrocardiography (ECG) represents the electrical potential that regulates heart muscle activity. (19,20) The potential is collected by electrodes placed on the skin's surface, allowing for the detection of various cardiac irregularities. ECG plays a vital role in diagnostic examinations in the field of cardiology.…”

Section: System Description and Methodology Dataset Visualizationmentioning

confidence: 99%

Extraction of fetal electrocardiogram signal based on K-means Clustering

Moutaib,

Fattah,

Farhaoui

et al. 2023

Data and Metadata

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Los electrocardiogramas (ECG) fetales proporcionan información crucial para las intervenciones y diagnósticos de las embarazadas a nivel clínico. Las señales maternas son robustas, lo que dificulta la recuperación y detección de ECGs fetales. En este artículo, proponemos una solución basada en Machine Learning adaptando el clustering de k-means para detectar el ECG fetal mediante la grabación de los ECGs. En nuestra primera parte de preprocesamiento, intentamos normalizar y segmentar la forma de onda del ECG. A continuación, utilizamos la distancia euclídea para medir la similitud. Para identificar un cierto número de centroides en nuestros datos, los resultados clasificados en dos clases se representan en la última parte mediante gráficos y se comparan con otros algoritmos, como el clasificador CNN, para demostrar la eficacia de este enfoque innovador, que puede aplicarse en tiempo real.

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Section: System Description and Methodology Dataset Visualizationmentioning

confidence: 99%

Extraction of fetal electrocardiogram signal based on K-means Clustering

Moutaib,

Fattah,

Farhaoui

et al. 2023

Data and Metadata

View full text Add to dashboard Cite

show abstract

“…Electrocardiography (ECG) graphically represents the electrical potential governing cardiac muscle activity. (17,18) Our primary objective in this study is to facilitate the visualization of ECG diagrams (figure 1) for physicians by introducing an image classification algorithm, (19,20) namely Convolutional Neural Networks (CNNs). This approach aims to assist in determining the category of fetal or maternal ECGs without encountering conflicts.…”

Section: System Descriptionmentioning

confidence: 99%

Fetal and Maternal Electrocardiogram ECG Prediction using Convolutional Neural Networks

Moutaib,

Fattah,

Farhaoui

et al. 2023

Data and Metadata

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Predicting fetal and maternal electrocardiograms (ECGs) is crucial in advanced prenatal monitoring. In this study, we explore the effectiveness of Convolutional Neural Networks (CNNs), using a carefully developed methodology to predict the category of fetal (F) or maternal (M) ECGs. In the first part, we trained a CNN model to predict fetal and maternal ECG images. In the following sections, the study results will be revealed. The CNN model demonstrated its ability to effectively discriminate between fetal and maternal patterns using automatically learned features.

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“…Recently, with the development of deep neural networks (DNNs), many researchers have tried to extract FECG signals using the DNN technique [ 20 ]. Zhong et al [ 21 ] proposed a deep convolutional encoder-decoder network to directly extract the FECG from the AECG signal.…”

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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A survey on FECG extraction using neural network and adaptive filter

Cited by 12 publications

References 22 publications

Extraction of fetal electrocardiogram signal based on K-means Clustering

Extraction of fetal electrocardiogram signal based on K-means Clustering

Fetal and Maternal Electrocardiogram ECG Prediction using Convolutional Neural Networks

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

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