Non-invasive fetal ECG extraction using discrete wavelet transform recursive inverse adaptive algorithm

BACKGROUND: Fetal heart activity adds significant information about the status of the fetus health. Early diagnosis of issues in the heart before delivery allows early intervention and significantly improves the treatment. OBJECTIVE: This paper presents a new adaptive filtering algorithm for fetal electrocardiogram (FECG) extraction from the maternal abdominal signal, known in literature as abdominal electrocardiogram (AECG) signal. Fetal QRS complex waves will be identified and extracted accurately for fetal health care and monitoring purposes. METHODS: We use discrete wavelet transform recursive inverse (DWT-RI) adaptive filtering algorithm for this objective. Thoracic maternal electrocardiogram (MECG) is used as a reference in the proposed algorithm and FECG components are extracted from AECG signal after suppressing the MECG projections. The proposed algorithm is compared to other typical adaptive filtering algorithms, least mean squares (LMS), recursive least squares (RLS), and recursive inverse (RI). RESULTS: Fetal QRS waveforms successful identification and extraction from AECG signal is evaluated objectively and visually and compared to other algorithms. We validated the proposed algorithm using both synthetic data and real clinical data. CONCLUSIONS: The proposed algorithm is capable of extracting fetal QRS waveforms successfully from AECG and outperforms other adaptive filtering algorithms in terms of accuracy and positive predictivity.

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Parallel Hardware-Software Architecture for computation of Discrete Wavelet Transform using the Recursive Merge Filtering algorithm

Lecture Notes in Computer Science - Parallel and Distributed Processing ◽

10.1007/3-540-45591-4_33 ◽

2000 ◽

pp. 250-256 ◽

Cited By ~ 2

Author(s):

Piyush Jamkhandi ◽

Amar Mukherjee ◽

Kunal Mukherjee ◽

Robert Franceschini

Keyword(s):

Wavelet Transform ◽

Software Architecture ◽

Discrete Wavelet Transform ◽

Discrete Wavelet ◽

Filtering Algorithm

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A real-time fetal ECG feature extraction using multiscale discrete wavelet transform

2012 5th International Conference on BioMedical Engineering and Informatics ◽

10.1109/bmei.2012.6512966 ◽

2012 ◽

Cited By ~ 11

Author(s):

K. D. Desai ◽

Manoj S. Sankhe

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Real Time ◽

Discrete Wavelet ◽

Fetal Ecg

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A novel variable filter band discrete wavelet transform: Application

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691314600078 ◽

2014 ◽

Vol 12 (04) ◽

pp. 1460007

Author(s):

Zhong Zhang ◽

Jin Ohtaki ◽

Hiroshi Toda ◽

Takashi Imamura ◽

Tetsuo Miyake

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Wavelet Packet ◽

Discrete Wavelet ◽

Wavelet Shrinkage ◽

Ecg Signals ◽

Fetal Ecg ◽

Band Filter ◽

Best Basis Algorithm ◽

Better Than

In this study, in order to verify the effectiveness of the variable filter band discrete wavelet transform (VFB-DWT) and construction method of the variable-band filter (VBF), a fetal ECG extraction has been carried out and the main results obtained are as follows. The approach to configuration VBF by selecting the frequency band only where the fetal ECG component is present was effective to configure the optimal base sensible signal. The extraction of the fetal ECG was successful by applying the wavelet shrinkage to VFB-DWT, which used the constructed VBF. The information entropy was selected as an evaluation index, and two kinds of ECG signals are used to evaluate the wavelet transform basis between the wavelet packet transform (WPT) and the VFB-DWT. One is a synthesized signal composed of white noise, the maternal ECG and the fetal ECG. The other signal is the real target signal separated by independent component analysis (ICA) and has the mother's body noise, the maternal ECG and the fetal ECG. The result shows that the basis by VBF of the VFB-DWT is better than the basis of the WPT that was chosen by the best basis algorithm (BBA).

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Eliminated aliasing effect on wavelet transform based multiresolution analysis

Geofísica Internacional ◽

10.22201/igeof.00167169p.2016.55.3.1721 ◽

2016 ◽

Vol 55 (3) ◽

Author(s):

Ernesto González-Flores ◽

José Oscar Campos-Enríquez ◽

Erick Camacho-Ramírez ◽

David Ernesto Rivera-Recillas

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Multiresolution Analysis ◽

Synthetic Data ◽

Seismic Signal ◽

Discrete Wavelet ◽

Frequency Bands ◽

Analysis Technique ◽

The Time Domain ◽

Seismic Signal Processing

Multiresolution analysis, based on the discrete wavelet transform, is here incorporated in seismic signal processing. This analysis technique enables decomposing a seismic signal, in different frequency bands, and thus to analyze the information contained in these frequency bands. Multiresolution analysis allows visualizing in the time domain the information contained in the frequency bands. Wavelets commonly used in the discrete wavelet transform present an overlay between scales, this constitutes an aliasing effect that gives rise to spurious effects. Vaidyanathan wavelet minimizes the overlay between scales. We applied this wavelet to synthetic data and to a 3D seismic cube. Accordingly, spurious effects from aliasing generated by overlay between scales are minimized with the Vaidyanathan wavelet.

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Discrete wavelet transform-based RI adaptive algorithm for system identification

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i3.pp2383-2391 ◽

2020 ◽

Vol 10 (3) ◽

pp. 2383

Author(s):

Mohammad Shukri Salman ◽

Alaa Eleyan ◽

Bahaa Al-Sheikh

Keyword(s):

Wavelet Transform ◽

System Identification ◽

Convergence Rate ◽

Discrete Wavelet Transform ◽

Adaptive Algorithm ◽

Convergence Rates ◽

Recursive Least Squares ◽

Discrete Wavelet ◽

Step Size ◽

Autocorrelation Matrix

In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

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