Combination of a FIR filter with a Genetic algorithm for the extraction of a fetal ECG

Talha, Malika Kedir; Guettouche, M.A.; Bousbia-Salah, A.

doi:10.1109/acssc.2010.5757842

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…(Kahankova et al, 2017c ). Adaptive methods have already been tested by our research group (Martinek and Zidek, 2012 ; Martinek et al, 2016a , 2017a , b ; Fajkus et al, 2017 ) and others (Kam and Cohen, 1999 ; Reaz and Wei, 2004 ; Al-Zaben and Al-Smadi, 2006 ; Poularikas and Zayed, 2006 ; Assaleh, 2007 ; Czabanski et al, 2008 ; Shadaydeh et al, 2008 ; Jia et al, 2010 ; Swarnalatha and Prasad, 2010 ; Talha et al, 2010 ; Amin et al, 2011 ; Niknazar et al, 2013 ; Wei et al, 2013 ; Martinek et al, 2016c ; Kahankova et al, 2017d , 2018 ). We need to emphasize that to achieve Morphological Analysis of fECG signals, adaptive methods appear to be more suitable.…”

Section: Discussionmentioning

confidence: 99%

“…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%

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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: Discussionmentioning

confidence: 99%

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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Fetal ECG extraction based on adaptive neuro-fuzzy interference system

Martínek

Skutova

Kahánková

et al. 2016

2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

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Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms

Martínek

Kahánková

Nazeran

et al. 2017

Sensors

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This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.

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Combination of a FIR filter with a Genetic algorithm for the extraction of a fetal ECG

Cited by 4 publications

References 3 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

Fetal ECG extraction based on adaptive neuro-fuzzy interference system

Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms

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