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

Kahánková, Radana; Mikolasova, Martina; Martínek, Radek

doi:10.1371/journal.pone.0266807

Cited by 17 publications

(6 citation statements)

References 38 publications

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“…For adaptive algorithms, these are mainly the filter order, convergence constant or forgetting factor [ 15 , 16 ]. We have found advantageous to use the optimization algorithms to find the optimal parameter setting instead of its manual selection in our previous studies [ 42 , 43 ]. This allows the algorithm to appropriately adjust the different parts of the hybrid system according to the week of gestation, the position of the fetus and other circumstances.…”

Section: Resultsmentioning

confidence: 99%

Independent component analysis algorithms for non-invasive fetal electrocardiography

Jaros,

Barnova,

Vilimkova Kahankova

et al. 2023

PLoS ONE

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The independent component analysis (ICA) based methods are among the most prevalent techniques used for non-invasive fetal electrocardiogram (NI-fECG) processing. Often, these methods are combined with other methods, such adaptive algorithms. However, there are many variants of the ICA methods and it is not clear which one is the most suitable for this task. The goal of this study is to test and objectively evaluate 11 variants of ICA methods combined with an adaptive fast transversal filter (FTF) for the purpose of extracting the NI-fECG. The methods were tested on two datasets, Labour dataset and Pregnancy dataset, which contained real records obtained during clinical practice. The efficiency of the methods was evaluated from the perspective of determining the accuracy of detection of QRS complexes through the parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). The best results were achieved with a combination of FastICA and FTF, which yielded mean values of ACC = 83.72%, SE = 92.13%, PPV = 90.16%, and F1 = 91.14%. Time of calculation was also taken into consideration in the methods. Although FastICA was ranked to be the sixth fastest with its mean computation time of 0.452 s, it had the best ratio of performance and speed. The combination of FastICA and adaptive FTF filter turned out to be very promising. In addition, such device would require signals acquired from the abdominal area only; no need to acquire reference signal from the mother’s chest.

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Section: Resultsmentioning

confidence: 99%

Independent component analysis algorithms for non-invasive fetal electrocardiography

Jaros,

Barnova,

Vilimkova Kahankova

et al. 2023

PLoS ONE

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“…The authors further stated that extraction was not successful for low quality of the aECG signals. In 8 , Kahankova et al tested and compared several combinations of LMS and RLS based adaptive algorithms with the ICA method and proposed an optimization scheme to increase their efficacy. The most accurate combination turned out to be one with the adaptive linear neuron (ADALINE), i.e.…”

Section: Discussionmentioning

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%

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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“…Moreover, both ANC algorithms necessitate MECG signals as a reference. The practicality of this algorithm in practical clinical and remote monitoring is limited [16].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN–BiLSTM Architecture

Yang,

Chen,

2024

Sensors

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The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model’s generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model’s discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely “Abdominal and Direct Fetal ECG Database” and “Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations”, resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper’s model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.

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

Cited by 17 publications

References 38 publications

Independent component analysis algorithms for non-invasive fetal electrocardiography

Independent component analysis algorithms for non-invasive fetal electrocardiography

Nature inspired method for noninvasive fetal ECG extraction

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN–BiLSTM Architecture

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