Extracting Fetal ECG Signals Through a Hybrid Technique Utilizing Two Wavelet-Based Denoising Algorithms

Developing an intelligent technique for fetal heartbeat detection to monitor the cardiac function of the fetus in the initial stages of pregnancy is crucial. In this research work, two hybrid algorithms are proposed that use a combination of recursive least square algorithm (RLS) and stationary wave...

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Main Authors: P. Darsana, Vaegae Naveen Kumar
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10229148/
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author P. Darsana
Vaegae Naveen Kumar
author_facet P. Darsana
Vaegae Naveen Kumar
author_sort P. Darsana
collection DOAJ
description Developing an intelligent technique for fetal heartbeat detection to monitor the cardiac function of the fetus in the initial stages of pregnancy is crucial. In this research work, two hybrid algorithms are proposed that use a combination of recursive least square algorithm (RLS) and stationary wavelet transform (SWT) for fetal ECG extraction. The goal of this research is to enhance the fetal ECG signal, reduce noise and artifact, and accurately detect the R-peaks by employing improved spatially selective noise filtration (ISSNF) method or threshold-based denoising approach in the wavelet domain. Accurate fetal R-peak detection can provide important clinical information and aid in the diagnosis and treatment of fetal heart conditions. The primary aim is to extract a clear fetal ECG signal from the mixed abdominal signal. The abdominal signal is divided into multiscale components using SWT, with different levels of noise determining the scale of wavelet decomposition. The RLS algorithm is then utilized for removing maternal ECG components, and either ISSNF or threshold-based algorithms are employed for denoising in the wavelet domain. We evaluate the effectiveness of our proposed method using both synthetic and clinical data. Our analysis involves qualitative and quantitative measures, including visual inspection, signal-to-noise ratio (SNR) computation, and QRS complex recognition. Our findings reveal that the proposed system exhibits superior performance when compared to conventional adaptive filtering techniques. The experimental results suggest that the proposed system has the potential to extract fetal ECG signals that are clear, with good SNR results and minimal disturbances.
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spelling doaj.art-ec760585f9694ef58b1dd3d76bdf36972023-09-05T23:00:31ZengIEEEIEEE Access2169-35362023-01-0111916969170810.1109/ACCESS.2023.330840910229148Extracting Fetal ECG Signals Through a Hybrid Technique Utilizing Two Wavelet-Based Denoising AlgorithmsP. Darsana0https://orcid.org/0009-0003-1804-4757Vaegae Naveen Kumar1https://orcid.org/0000-0002-0292-697XSchool of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, IndiaDeveloping an intelligent technique for fetal heartbeat detection to monitor the cardiac function of the fetus in the initial stages of pregnancy is crucial. In this research work, two hybrid algorithms are proposed that use a combination of recursive least square algorithm (RLS) and stationary wavelet transform (SWT) for fetal ECG extraction. The goal of this research is to enhance the fetal ECG signal, reduce noise and artifact, and accurately detect the R-peaks by employing improved spatially selective noise filtration (ISSNF) method or threshold-based denoising approach in the wavelet domain. Accurate fetal R-peak detection can provide important clinical information and aid in the diagnosis and treatment of fetal heart conditions. The primary aim is to extract a clear fetal ECG signal from the mixed abdominal signal. The abdominal signal is divided into multiscale components using SWT, with different levels of noise determining the scale of wavelet decomposition. The RLS algorithm is then utilized for removing maternal ECG components, and either ISSNF or threshold-based algorithms are employed for denoising in the wavelet domain. We evaluate the effectiveness of our proposed method using both synthetic and clinical data. Our analysis involves qualitative and quantitative measures, including visual inspection, signal-to-noise ratio (SNR) computation, and QRS complex recognition. Our findings reveal that the proposed system exhibits superior performance when compared to conventional adaptive filtering techniques. The experimental results suggest that the proposed system has the potential to extract fetal ECG signals that are clear, with good SNR results and minimal disturbances.https://ieeexplore.ieee.org/document/10229148/ECG extractionfetal ECGimproved spatially selective noise filtrationrecursive least square algorithmstationary wavelet transformsthreshold-based algorithm
spellingShingle P. Darsana
Vaegae Naveen Kumar
Extracting Fetal ECG Signals Through a Hybrid Technique Utilizing Two Wavelet-Based Denoising Algorithms
IEEE Access
ECG extraction
fetal ECG
improved spatially selective noise filtration
recursive least square algorithm
stationary wavelet transforms
threshold-based algorithm
title Extracting Fetal ECG Signals Through a Hybrid Technique Utilizing Two Wavelet-Based Denoising Algorithms
title_full Extracting Fetal ECG Signals Through a Hybrid Technique Utilizing Two Wavelet-Based Denoising Algorithms
title_fullStr Extracting Fetal ECG Signals Through a Hybrid Technique Utilizing Two Wavelet-Based Denoising Algorithms
title_full_unstemmed Extracting Fetal ECG Signals Through a Hybrid Technique Utilizing Two Wavelet-Based Denoising Algorithms
title_short Extracting Fetal ECG Signals Through a Hybrid Technique Utilizing Two Wavelet-Based Denoising Algorithms
title_sort extracting fetal ecg signals through a hybrid technique utilizing two wavelet based denoising algorithms
topic ECG extraction
fetal ECG
improved spatially selective noise filtration
recursive least square algorithm
stationary wavelet transforms
threshold-based algorithm
url https://ieeexplore.ieee.org/document/10229148/
work_keys_str_mv AT pdarsana extractingfetalecgsignalsthroughahybridtechniqueutilizingtwowaveletbaseddenoisingalgorithms
AT vaegaenaveenkumar extractingfetalecgsignalsthroughahybridtechniqueutilizingtwowaveletbaseddenoisingalgorithms