research-article

Method of Non-invasive Fetal Electrocardiogram Denoising Based on Stationary Wavelet Transform and Spatially Selective Noise Filtration

Authors:

Shuai YuAuthors Info & Claims

BIBE2020: Proceedings of the Fourth International Conference on Biological Information and Biomedical Engineering

Article No.: 19, Pages 1 - 5

https://doi.org/10.1145/3403782.3403801

Published: 21 July 2020 Publication History

BIBE2020: Proceedings of the Fourth International Conference on Biological Information and Biomedical Engineering

Method of Non-invasive Fetal Electrocardiogram Denoising Based on Stationary Wavelet Transform and Spatially Selective Noise Filtration

Pages 1 - 5

Abstract
References

Abstract

The non-invasive fetal electrocardiogram can provide significant clinical diagnostic information for the fetal health condition. However, since the signal is collected from the abdomen of the pregnant woman, its signal-to-noise ratio is low and it contains a lot of noise, which causes much trouble for the extraction of the fetal electrocardiogram. The non-invasive fetal electrocardiogram is non-stationary, and the noise may overlap with the useful signal in the frequency domain. To solve this challenging denoising work, we propose a method based on stationary wavelet transform and spatially selective noise filtration. The method is evaluated in the synthetic dataset and clinic dataset, and the experiment results show that this method performs better than other two methods. Moreover, the filtered signal is clearer, and the contour is smoother, which is helpful for fetal electrocardiogram morphological analysis and visualization.

References

[1]

Martin, Chester B. Electronic fetal monitoring: a brief summary of its development, problems and prospects. European Journal of Obstetrics and Gynecology and Reproductive Biology 78.2(1998), 0--140.

[2]

Bernardes, J. and A. Costa-Pereira. Efficacy and safety of intrapartum electronic fetal monitoring: an update. Obstetrics and Gynecology 87.3(1996):476; author reply 477.

[3]

Taffel, Selma M., et al. 1989 U.S. Cesarean Section Rate Steadies VBAC Rate Rises to Nearly One in Five. Birth 18.2(1991), 73--77.

[4]

Clifford, Gari D, et al. (2014). Non-invasive fetal ECG analysis. Physiological Measurement 35.8, 1521--1536.

[5]

De Lathauwer, L., De Moor, B, & Vandewalle, J. (2000). Fetal electrocardiogram extraction by blind source subspace separation. IEEE transactions on bio-medical engineering, 47(5), 567--572.

[6]

Widrow, B. Jr, J. R. G., Mccool, J. M., Kaunitz, J., & Goodlin, R. C. (1976). Adaptive noise cancelling: principles and applications. Proceedings of the IEEE, 63(12), 1692--1716.

[7]

Kanjilal, P. P, Palit, S., & Saha, G. (1997). Fetal ecg extraction from single-channel maternal ecg using singular value decomposition. IEEE Transactions on Biomedical Engineering, 44(1), 51--59.

[8]

Behar, Clifford, Gari D. (2014). Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data. Physiological Measurement, 35, 1569--1589.

[9]

Castillo E, Morales DP, García A, Martínez-Martí F, Parrilla L, Palma AJ. (2013) Noise Suppression in ECG Signals through Efficient One-Step Wavelet Processing Techniques. Journal of Applied Mathematics. Volume 2013.

[10]

Jin, D, Wang, Y, & ang, W. (2007). A wavelet space based approach for doppler ultasound blood signals separation. Chinese Journal of Acoustcs, 26(3), 261--268.

[11]

Andreotti, F, Behar, J, Zaunseder, S., Oster, J., & Clifford, G. D. (2016). An open-source framework for stress-testing non-invasive foetal ecg extraction algorithms. Physiological Measurement, 37(5), 627--648.

[12]

Moor BD, Gersem PD, Schutter BD, Favoreel W (1997) DAISY: a database for identification of systems. J A 30(3):4--5

[13]

Liu, L, & Jiang, J. (2011). Using stationary wavelet transformation for signal denoising. Eighth International Conference on Fuzzy Systems & Knowledge Discovery. IEEE.

[14]

Xu, Y., Weaver, J. B., Healy, D. M., & Lu, J. (1994). Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 3(6), 747--758.

Digital Library

[15]

Li, Q, Mark, R. G, & Clifford, G. D. (2008). Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a kalman filter. Physiological Measurement, 29(1), 15--32.

Cited By

Zhao XLiu DMa LAi QLiu QXie S(2020)EEG Signals De-Noising with Wavelet by Optimizing Threshold Based on Fruit Fly OptimizationProceedings of the 2020 9th International Conference on Networks, Communication and Computing10.1145/3447654.3447665(71-77)Online publication date: 18-Dec-2020
https://dl.acm.org/doi/10.1145/3447654.3447665

Index Terms

Method of Non-invasive Fetal Electrocardiogram Denoising Based on Stationary Wavelet Transform and Spatially Selective Noise Filtration
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics

Recommendations

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of ...
Research on Spatially Selective Noise Filtration Based Wavelet Transform
ICICTA '10: Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 03

Around the problem that the wavelet transform modulus maxima de-noising method algorithm has an inferior stability as well as a large number of calculations, another de-noising method based on transmission characteristics of wavelet transform at ...
Research of fetal ECG extraction using wavelet analysis and adaptive filtering

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

BIBE2020: Proceedings of the Fourth International Conference on Biological Information and Biomedical Engineering

July 2020

219 pages

ISBN:9781450377096

DOI:10.1145/3403782

Conference Chair:
Weimiao Yu,
Program Chair:
Hui Sun

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

ASciE: Association for Science and Engineering

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 July 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

National Natural Science Foundation of China

Conference

BIBE2020

BIBE2020: The Fourth International Conference on Biological Information and Biomedical Engineering

July 21 - 23, 2020

Chengdu, China

Acceptance Rates

BIBE2020 Paper Acceptance Rate 36 of 116 submissions, 31%;

Overall Acceptance Rate 36 of 116 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
33
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhao XLiu DMa LAi QLiu QXie S(2020)EEG Signals De-Noising with Wavelet by Optimizing Threshold Based on Fruit Fly OptimizationProceedings of the 2020 9th International Conference on Networks, Communication and Computing10.1145/3447654.3447665(71-77)Online publication date: 18-Dec-2020
https://dl.acm.org/doi/10.1145/3447654.3447665

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten