System for adaptive extraction of non-invasive fetal electrocardiogram

Barnova, Katerina; Martínek, Radek; Jaroš, René; Kahánková, Radana; Behbehani, Khosrow; Snåšel, Václav

doi:10.1016/j.asoc.2021.107940

Cited by 25 publications

(13 citation statements)

References 76 publications

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“…Barnova et al introduced several algorithms, e.g. [ 10 , 11 , 41 ] combining EMD-based methods with different adaptive algorithms (such as RLS and FTF). The efficacy of the ICA-RLS-EMD algorithm introduced in [ 10 ] was tested on Challenge dataset, reaching Se = 81.79%, PPV = 87.16%, and F1 = 84.08%.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, when testing on Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of (only) 25 selected recordings. The best results were achieved for ICA-FTF-CEEMDAN method introduced in [ 41 ], which was tested on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations (FECGDARHA) database (average values of ACC 92.98%). On the Challenge 2013 database, the method achieved average values of ACC = 78.47%.…”

Section: Discussionmentioning

confidence: 99%

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Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction

2022

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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 of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction

2022

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“…This is caused by the fact that the database initially contained 5 recordings (r01, r04, r07, r08, and r10). However, some authors 6 also tested the remaining recordings (r01–r12) that were published later in 24 , named Labour dataset. Therefore Tab.…”

Section: Discussionmentioning

confidence: 99%

“…Many different methods have been introduced for fECG signal extraction from abdominal ECG signals 1 , 4 – 6 . Adaptive filters are known to produce promising results 1 .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it could be quite complicated to maintain the signal’s high quality in the clinical practice. For this reason, several authors 6 – 8 used an alternative approach to adaptive extraction, where the reference maternal signal is estimated directly from the abdominal inputs by using, for example, blind source separation, such as Independent component analysis (ICA) 9 or Principal Component Analysis (PCA) 10 , 11 . Both PCA and ICA methods decompose the input signal into its source components.…”

Section: Introductionmentioning

confidence: 99%

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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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