000097017 001__ 97017
000097017 005__ 20230519145349.0
000097017 0247_ $$2doi$$a10.1016/j.cmpb.2020.105856
000097017 0248_ $$2sideral$$a120852
000097017 037__ $$aART-2021-120852
000097017 041__ $$aeng
000097017 100__ $$aHossain, Md-Billal
000097017 245__ $$aA robust ECG denoising technique using variable frequency complex demodulation
000097017 260__ $$c2021
000097017 5060_ $$aAccess copy available to the general public$$fUnrestricted
000097017 5203_ $$aBackground and Objective
Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time.
Methods
This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering.
Results
Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms.
Conclusions
The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation.
000097017 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T96$$9info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 745755-WECARMON
000097017 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000097017 590__ $$a7.027$$b2021
000097017 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b20 / 112 = 0.179$$c2021$$dQ1$$eT1
000097017 591__ $$aMEDICAL INFORMATICS$$b6 / 31 = 0.194$$c2021$$dQ1$$eT1
000097017 591__ $$aENGINEERING, BIOMEDICAL$$b20 / 98 = 0.204$$c2021$$dQ1$$eT1
000097017 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b12 / 111 = 0.108$$c2021$$dQ1$$eT1
000097017 594__ $$a9.7$$b2021
000097017 592__ $$a1.329$$b2021
000097017 593__ $$aHealth Informatics$$c2021$$dQ1
000097017 593__ $$aComputer Science Applications$$c2021$$dQ1
000097017 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000097017 700__ $$aBashar, Syed Khairul
000097017 700__ $$0(orcid)0000-0001-8742-0072$$aLazaro, Jesús$$uUniversidad de Zaragoza
000097017 700__ $$aReljin, Natasa
000097017 700__ $$aNoh, Yeonsik
000097017 700__ $$aChon, Ki Han
000097017 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000097017 773__ $$g200, 105856 (2021), [27 pp.]$$pComput. methods programs biomed.$$tComputer Methods and Programs in Biomedicine$$x0169-2607
000097017 8564_ $$s1347406$$uhttps://zaguan.unizar.es/record/97017/files/texto_completo.pdf$$yPostprint
000097017 8564_ $$s262222$$uhttps://zaguan.unizar.es/record/97017/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000097017 909CO $$ooai:zaguan.unizar.es:97017$$particulos$$pdriver
000097017 951__ $$a2023-05-18-13:24:55
000097017 980__ $$aARTICLE