Comparison of Some EMD based Technique for Baseline Wander Correction in Fetal ECG Signa

Ghosh, Prasanta Kumar; Poonia, Deepika

doi:10.5120/20416-2836

Cited by 6 publications

(19 citation statements)

References 9 publications

(14 reference statements)

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“…Ghosh and Poonia in 2015 [ 163 ] compared some of the EMD based techniques for fECG signal processing (reduction of the baseline wander). For comparison purposes they used the EMD, the ensemble EMD (EEMD) and the EMD based methods.…”

Section: Fetal Electrocardiographymentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

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Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

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Section: Fetal Electrocardiographymentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The second condition is that at any point the mean value of the envelopes defined by the local maxima and the local minima must be zero [21]. The process of signal decomposition into IMFs is called sifting process and can be described by the following steps [22], [23].…”

Section: A Empirical Mode Decompositionmentioning

confidence: 99%

“…This mean value is subtracted from the input signal s(t). The first proto-IMF p 01 (t) [22], [23] is obtained according to equation (2):…”

Section: A Empirical Mode Decompositionmentioning

confidence: 99%

“…Unfortunately, it does not often occur that the p 01 (t) component qualifies for IMF and it is, therefore, marked as proto-IMF at this moment. The above-stated procedure must, therefore, be repeated [22], [23]; it is generally represented by equation (3):…”

Section: A Empirical Mode Decompositionmentioning

confidence: 99%

“…where k indicates the iteration index and i the index of the extracted IMF. The procedure can theoretically be repeated until the signal size p ik (t) is constant [23]. For this reason, it is necessary to define the stopping criterion.…”

Section: A Empirical Mode Decompositionmentioning

confidence: 99%

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Hybrid Methods Based on Empirical Mode Decomposition for Non-Invasive Fetal Heart Rate Monitoring

et al. 2020

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This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy (ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.INDEX TERMS Non-invasive fetal electrocardiography, fetal heart rate, hybrid methods, empirical mode decomposition (EMD), independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS).

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Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

et al. 2020

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Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative lowcost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|∆T i |), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC > 95% was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73%, SE = 91.57%, PPV = 94.80% and F1 = 93.12%. Using the EEMD method, ACC > 95% was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49%, SE = 97.89%, PPV = 99.53% and F1 = 98.69%. In this study, the best results were achieved using the AWT method, which provided ACC > 95% in all 12 types and levels of interference with average values of ACC = 99.34%, SE = 99.49%, PPV = 99.85% a F1 = 99.67%. INDEX TERMSFetal phonocardiography (fPCG), fetal heart rate (fHR), non-invasive fetal monitoring, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), adaptive wavelet transform (AWT).

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Comparison of Some EMD based Technique for Baseline Wander Correction in Fetal ECG Signa

Cited by 6 publications

References 9 publications

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Hybrid Methods Based on Empirical Mode Decomposition for Non-Invasive Fetal Heart Rate Monitoring

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

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