A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals

Taha, Luay Yassin; Abdel-Raheem, Esam

doi:10.3390/s20123536

Cited by 17 publications

(12 citation statements)

References 45 publications

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“…In addition, the method has proven to be cost-effective and therefore suitable for implementation within a battery-powered remote monitoring device. Research of Taha et al 39 introduced a blind source separation-based method, and a new null space idempotent transformation matrix (NSITM) algorithm. When testing the method on signals from the Challenge 2013 dataset, an average value of ACC = 97.00% was achieved.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

“…Research of Taha et al 39 introduced a blind source separation-based method, and a new null space idempotent transformation matrix (NSITM) algorithm. When testing the method on signals from the Challenge 2013 dataset, an average value of ACC = 97.00% was achieved.…”

Section: Discussionmentioning

confidence: 99%

Nature inspired method for noninvasive fetal ECG extraction

Raj

Brablík

Kahánková

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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“…Blind source separation (BSS), sometimes referred to as blind signal processing, is capable of recovering a source signal from an observed signal in the absence of critical information, such as source and channel [ 1 , 2 , 3 ]. Due to its high adaptability and other advantages, BSS has been employed in a variety of research fields in recent years, such as image processing, medical evaluation, radar analysis, speech recognition, and machinery [ 4 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

Xia

Ding

Zhang

et al. 2022

Sensors

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The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms.

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“…However, abdominal mixed signals of pregnant women are complex, comprising MECG signals, FECG signals, baseline drift, power frequency interference, and noises [ 4 ]. Blind source separation (BSS) [ 5 ] can be used to extract FECG signals from the abdominal mixed signals, but there is a large amount of residual noises in the extracted FECG signals.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results showed that TS combined with the FastICA algorithm yielded the best performance, with a high signal-to-noise ratio (SNR). The FastICA algorithm uses approximate negative entropy and Newton iterative methods to reduce the amount of computation, which has the advantage of fast convergence and is a widely used signal separation method [ 5 ]. However, its convergence performance is greatly affected by the initial weight [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Hao

Yang

Zhou

et al. 2022

Sensors

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Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

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A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals

Cited by 17 publications

References 45 publications

Nature inspired method for noninvasive fetal ECG extraction

Nature inspired method for noninvasive fetal ECG extraction

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

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