Fetal ECG Extraction from Maternal Abdominal ECG Using Neural Network

Hasan, Muhammad Kamil Che; Ibrahimy, Muhammad Ibn; Reaz, Mamun Bin Ibne

doi:10.4236/jsea.2009.25043

Cited by 23 publications

(9 citation statements)

References 10 publications

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“…Nonlinear methods are based on Artificial Intelligence (AI) and those that have been applied to extract the NI-fECG signals include: Adaptive Neural Networks (ANN), Hasan et al ( 2009a ), Hybrid Neural Networks (HNN), Assaleh ( 2007 ), Genetic Algorithms and Bayesian Adaptive Filtering Frameworks Talha et al ( 2010 ), Kam and Cohen ( 1999 ), as well as techniques utilizing Adaptive Neuro-Fuzzy Interference System (ANFIS), Assaleh ( 2007 ), Al-Zaben and Al-Smadi ( 2006 ), Martinek et al ( 2016c ), and Martinek and Zidek ( 2012 ). In addition, Artificial Neural Networks based on Logical Interpretation of fuzzy if-then Rules (ANBLIR) has been introduced as an approach to classify fetal cardiotocograms Czabanski et al ( 2008 ).…”

Section: State Of the Artmentioning

confidence: 99%

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring

et al. 2018

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Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01–real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direct Fetal Electrocardiogram Database), which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data (i.e., around 88,000 R waves) for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work.

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Section: State Of the Artmentioning

confidence: 99%

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring

et al. 2018

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“…37 Neural network is another technique for adaptively extracting fECG from faLead. 38 The input signal is considered as mECG and the target signal is faLead. The suppression of amECG from faLead occurs by correlative detraction so that the output can be considered as only fECG.…”

Section: Adaptive Filtering For Fetal Ecg Extractionmentioning

confidence: 99%

Noninvasive Fetal Electrocardiography: An Overview of the Signal Electrophysiological Meaning, Recording Procedures, and Processing Techniques

Agostinelli

Grillo

Biagini

et al. 2015

Noninvasive Electrocardiol

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Differences among methods rely on different techniques used to extract fECG. If pure abdominal electrode configurations are used, fECG is extracted directly from the abdominal recording using independent component analysis or template subtraction. Eventually, if mixed electrode configurations are used, the fECG can be extracted using the adaptive filtering fed with the maternal ECG recorded by the electrodes located in the woman thorax or shoulder.

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“…The Implementation of QSW with Braun multiplier and array multiplier is presented in table (3) and (4) respectively. The respective comparative charts are presented in figure (10) and (11).…”

Section: G R-peak Detection and Heart Rate Analysismentioning

confidence: 99%

Utility Based Portfolio Optimization through Integer Programming Under Stochastic Markets

Arif H*.,

Pakkala

2019

IJRTE

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During labor ECG Monitoring is one of the most used method to determine the condition of the fetus. The type of monitoring varies from patients to patients. Few require continuous monitoring because of medication while others require only intermittent monitoring. The fetal ECG is the only information source in early stage diagnosis of fetal health and status. This paper describes the implementation of a system based on FPGA which denoises the abdominal ECG and separates the Fetal ECG from the abdominal signal. For preprocessing a VLSI hardware in FPGA for wavelet transform method is designed and implemented. The embedded architecture on FPGA is based on quadrature spline wavelet transform. FPGA implementation of quadrature spline wavelet transform filter was done with different multipliers. The extraction of fetal electrocardiogram signal was done using slope threshold and two stage template search method where the fetal ECG is extracted from the abdominal ECG. The logical elements, power and delay for the proposed architecture is reported in this paper. For implementation Cyclone II kit and Quartus software was used. In future the classification method will be implemented.

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Fetal ECG Extraction from Maternal Abdominal ECG Using Neural Network

Abstract: FECG (Fetal ECG)

Cited by 23 publications

References 10 publications

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring

Noninvasive Fetal Electrocardiography: An Overview of the Signal Electrophysiological Meaning, Recording Procedures, and Processing Techniques

Utility Based Portfolio Optimization through Integer Programming Under Stochastic Markets

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