Blind Maternal-Fetal ECG Separation Based on the Time-Scale Image TSI and SVD – ICA Methods

Ziani, Said; Jbari, Atman; Bellarbi, Larbi; Farhaoui, Yousef

doi:10.1016/j.procs.2018.07.179

Cited by 30 publications

(17 citation statements)

References 5 publications

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“…Second, the background noise has a considerable affect on its performance [ 3 ]. Some works were reported to combine the FastICA approach with other approaches [ 9 , 10 , 27 , 28 ].…”

Section: Related Fecg Extraction Methodsmentioning

confidence: 99%

“…In general, the extraction algorithms can be classified as either spatial (non adaptive) or temporal (adaptive) algorithms [ 7 ]. Examples of the BSS/BSE based non-adaptive algorithms include principal component analysis (PCA) [ 8 ], independent component analysis (ICA) [ 8 , 9 ], time scale image (TSI) and singular value decomposition (SVD)/ICA [ 10 ], periodic component analysis [ 11 ], parallel linear predictor (PLP) filters [ 12 , 13 ], template subtraction (TS) [ 14 ], artificial neural network (ANN) [ 15 ], support vector regression (SVR) [ 16 ], tensor decomposition (TD) [ 17 ], deflation [ 18 ], adaptive comb filter (ACF) [ 19 ], and null space component (NCA) [ 20 ]. Examples of the adaptive algorithms include the multi-sensory adaptive noise canceller (MSANC) [ 7 ], fast adaptive orthogonal group ICA [ 21 ], adaptive Volterra filter (AVF) [ 22 ], adaptive neuro fuzzy inference system (ANFIS) and wavelet transform [ 23 ], Kalman filtering [ 24 ], event synchronous canceller [ 25 ], and type-2 adaptive neuro-fuzzy inference systems [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the work in [ 9 ] used ICA to separate FECG signals and detect and classify of mother and fetal heart beats based on compressive sensing (CS) theory. ICA has been also used together with the wavelet decomposition [ 27 ], TSI and SVD [ 10 ], and adaptive noise cancellation [ 28 ], to extract FECG signals.…”

Section: Introductionmentioning

confidence: 99%

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

Taha

Abdel-Raheem

2020

Sensors

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This paper presents a new non-invasive deterministic algorithm of extracting the fetal Electrocardiogram (FECG) signal based on a new null space idempotent transformation matrix (NSITM). The mixture matrix is used to compute the ITM. Then, the fetal ECG (FECG) and maternal ECG (MECG) signals are extracted from the null space of the ITM. Next, MECG and FECG peaks detection, control logic, and adaptive comb filter are used to remove the unwanted MECG component from the raw FECG signal, thus extracting a clean FECG signal. The visual results from Daisy and Physionet real databases indicate that the proposed algorithm is effective in extracting the FECG signal, which can be compared with principal component analysis (PCA), fast independent component analysis (FastICA), and parallel linear predictor (PLP) filter algorithms. Results from Physionet synthesized ECG data show considerable improvement in extraction performances over other algorithms used in this work, considering different additive signal-to-noise ratio (SNR) increasing from 0 dB to 12 dB, and considering different fetal-to-maternal SNR increasing from −30 dB to 0 dB. The FECG detection of the NSITM is evaluated using statistical measures and results show considerable improvement in the sensitivity (SE), the accuracy (ACC), and the positive predictive value (PPV), as compared with other algorithms. The study demonstrated that the NSITM is a feasible algorithm for FECG extraction.

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“…Second, the background noise has a considerable affect on its performance [ 3 ]. Some works were reported to combine the FastICA approach with other approaches [ 9 , 10 , 27 , 28 ].…”

Section: Related Fecg Extraction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Taha

Abdel-Raheem

2020

Sensors

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“…Rasooli et al [35] used SG for frequency and entropy-based feature and Acharya et al [36] used the resized (128 ×128 pixels) 2D-SG to improve performance. 3-D SG for feature extraction is used in Mourad and Fethi [37], Awal et al [38], Ziani et al [39], Sukiennik and Białasiewicz [40], Bukkapatnam et al [41], and Hagiwara et al [42]. Also, RPCA, which is an improved PCA algorithm, was suggested by Wright et al [43] in 2009.…”

Section: Introductionmentioning

confidence: 99%

Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals

Lee

Kwak

2019

IEEE Access

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This paper is concerned with personal identification using a robust EigenECG network (REECGNet) based on time-frequency representations of electrocardiogram (ECG) signals. For this purpose, we use a robust principal component analysis network (RPCANet) and wavelet analysis. In general, PCA performance and applicability in real case scenarios is limited by the lack of robustness to outliers and corrupted observations. However, in a real nonstationary ECG noise environment, RPCA shows good performance when the method is applied with variable dimensions of local signal subspaces. That is why RPCA-based ECG identification is extremely robust with nonlinear data. Also, a REECGNet performs well without back-propagation to obtain features from the visual content. We constructed a Chosun University ECG Database (CU-ECG DB) and compared with the Physikalisch-Technische Bundesanstalt ECG database (PTB-ECG DB), which is shared data. Finally, the experimental results show the advantages and effectiveness of the applied recognition scheme with 98.25% performance. In addition, to demonstrate the superiority of REECGNet, we experimented with adding noise and the experimental result showed 97.5% recognition rate. INDEX TERMS Robust EigenECG network, scalogram, ECG biometrics, person identification.

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“…The existing ECG detection methodologies aim to improve the detection accuracy via sophisticated signal processing techniques. Some of the popular detection methodologies can be numerated as spectrum analysis methods (Fourier transform and short-time Fourier transform) [9], [10], mathematical morphology [11], wavelet transform (WT) [12], empirical mode decomposition (EMD) [13], blind source separation [14], and sparsity based strategies [15].…”

Section: Introductionmentioning

confidence: 99%

Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

Chen

Zeng

2019

IEEE Access

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Dyadic wavelet transform is useful in analyzing electrocardiogram (ECG) signals due to its fast computation and its multiresolution ability. In order to improve the feature extraction performance of dyadic wavelet transform, a new construction example of centralized multiresolution (CMR) is proposed. The proposed CMR example consists of two elements, namely, a dyadic part and a non-dyadic part. The dyadic part, based on the maximal overlap second generation wavelet packet transform (SGWPT), generates dyadic wavelet packets. The non-dyadic part engenders ensemble wavelet packets by postprocessing on the dyadic part. The produced wavelet packets and ensemble wavelet packets are combined to realize continued spectral refinement around fixed central analysis frequencies. Numerical simulation and a case study of ECG signal decomposition are utilized to validate the enhancements of the proposed CMR example. The processing results of the CMR example are compared with those of the dual tree complex wavelet transform and the conventional SGWPT. It is validated this CMR example achieves better feature extraction performances due to the presence of the exact translation invariance property.

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Blind Maternal-Fetal ECG Separation Based on the Time-Scale Image TSI and SVD – ICA Methods

Cited by 30 publications

References 5 publications

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

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

Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals

Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

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