Literature DB >> 9735560

Denoising of the uterine EHG by an undecimated wavelet transform.

P Carré1, H Leman, C Fernandez, C Marque.   

Abstract

We propose two original methods of denoising of the uterine electrohysterography (EHG) signal by wavelets. This external electrophysiological signal is corrupted by electronic, electromagnetic noises and by the remaining electrocardiogram of the mother. The interfering signals have overlapping spectra. Therefore, a classical filtering is unusable. Wavelets should be a very well-suited denoising tool. The first proposed method uses the algorithm "à trou" with nonsymmetrical filters. The computation is rapid and the results are satisfying compared to the classical denoising techniques. The second algorithm is an improvement of the first method. It uses orthogonal wavelets and the result of the thresholding corresponds to the average of all circulant shifts denoised by a decimated wavelet transform. Results are compared to traditional denoising algorithms by wavelet (orthogonal, maximally decimated). The proposed algorithms are more efficient on simulated signals as well as on uterine EHG.

Mesh:

Year:  1998        PMID: 9735560     DOI: 10.1109/10.709554

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  7 in total

1.  A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups.

Authors:  G Fele-Zorz; G Kavsek; Z Novak-Antolic; F Jager
Journal:  Med Biol Eng Comput       Date:  2008-04-24       Impact factor: 2.602

2.  Developing a real time electrocardiogram system using virtual bio-instrumentation.

Authors:  Khalifa Elmansouri; Rachid Latif; Boujamaa Nassiri; Fadel Mrabih Rabou Maoulainine
Journal:  J Med Syst       Date:  2014-04-05       Impact factor: 4.460

3.  Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury.

Authors:  Matthew Nitzken; Nihit Bajaj; Sevda Aslan; Georgy Gimel'farb; Ayman El-Baz; Alexander Ovechkin
Journal:  J Biomed Sci Eng       Date:  2013-07-18

4.  Prediction of preterm deliveries from EHG signals using machine learning.

Authors:  Paul Fergus; Pauline Cheung; Abir Hussain; Dhiya Al-Jumeily; Chelsea Dobbins; Shamaila Iram
Journal:  PLoS One       Date:  2013-10-28       Impact factor: 3.240

5.  Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

Authors:  Paul Fergus; David Hignett; Abir Hussain; Dhiya Al-Jumeily; Khaled Abdel-Aziz
Journal:  Biomed Res Int       Date:  2015-01-29       Impact factor: 3.411

6.  Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram.

Authors:  Daniela Martins; Arnaldo Batista; Helena Mouriño; Sara Russo; Filipa Esgalhado; Catarina R Palma Dos Reis; Fátima Serrano; Manuel Ortigueira
Journal:  Sensors (Basel)       Date:  2022-10-09       Impact factor: 3.847

Review 7.  Electrodes in external electrohysterography: a systematic literature review.

Authors:  Thierry R Jossou; Aziz Et-Tahir; Zakaria Tahori; Abdelmajid El Ouadi; Daton Medenou; Abdelmajid Bybi; Latif Fagbemi; Mohamed Sbihi; Davide Piaggio
Journal:  Biophys Rev       Date:  2021-05-09
  7 in total

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