ECG statistical denoising in the wavelet domain

Arvinti, Beatrice; Costache, Marius; Toader, Dumitru; Oltean, Marius; Isar, Alexandru

doi:10.1109/isetc.2010.5679269

Cited by 20 publications

(9 citation statements)

References 7 publications

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“…The major sources of noise that usually defile ECG signals are: Baseline wander (BW), Power line interference (PLI), Electromyography noise (EMG), Electrode contact noise, Motion artifacts and Instrumentation noise generated by electrosurgical noise. [2][3].…”

Section: A Review On Noises In Ecg Signalsmentioning

confidence: 99%

A Survey on Ecg Signal Denoising Using S-Transform and Sg Filtering

Muthusivagami¹,

Prof²,

Electronics³

2020

IJAR

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Section: A Review On Noises In Ecg Signalsmentioning

confidence: 99%

A Survey on Ecg Signal Denoising Using S-Transform and Sg Filtering

Muthusivagami¹,

Prof²,

Electronics³

2020

IJAR

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“…Furthermore, the best threshold selection rule and decomposition level should be found. The DWT algorithm has the following three steps [8], [5]: _ The DWT is applied on a noisy signal.…”

Section: Complexity Of Beads Algorithmmentioning

confidence: 99%

E. Service-Oriented Architecture Solution for ECG Signal Processing

Blažauskas

Muliuolis²,

Bikulčienė³

et al. 2017

ITC

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This paper introduces a distributed service-oriented system, which is developed to provide ECG (electrocardiogram) monitoring, analysis and storage services. The Service-Oriented Architecture system design is introduced for ECG signal transmission and processing. The implementation of cloud-based web-services and overall system architecture is described. The presented system includes a T-shirt with five electrodes intended for the acquisition of the signal. The ECG data for the experiment were recorded while the participant was moving. The signal replicates real conditions and the ECG data contain different high and low frequency noise. Therefore, this paper includes analysis of data filtering methods, model selection and ECG parameter calculation algorithms. The DWT algorithm was selected for the high frequency noise reduction and the BEADS method was used for trend removal. It was experimentally identified that these algorithms are effective and can be used in the system under development. The tests covering overall system were performed on an Amazon cloud computing infrastructure. The results are presented together with a discussion of various constraints of service-oriented performance.

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“…A baseline wander correction method [13] and a denoising algorithm [14] are applied on the signal, the resulted corrected ECG being the input of the compression algorithm.…”

Section: The Proposed Compression Algorithmmentioning

confidence: 99%

Optimization of an electrocardiogram compression technique based on wavelets

Arvinti

Isar

Toader

et al. 2012

2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)

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The paper presents the performance of a compression technique designed for electrocardiograms which is of interest due to the actual development of remote monitoring systems. The algorithm is based on wavelets and has been tested on an extended database to outline the performances and enable evaluation. Several features, like class of mother wavelets, number of vanishing moments and quantization level have been taken into consideration, the main aim of the paper being to define an optimization algorithm to allow the implementation of the compression technique in an automated system.

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ECG statistical denoising in the wavelet domain

Cited by 20 publications

References 7 publications

A Survey on Ecg Signal Denoising Using S-Transform and Sg Filtering

A Survey on Ecg Signal Denoising Using S-Transform and Sg Filtering

E. Service-Oriented Architecture Solution for ECG Signal Processing

Optimization of an electrocardiogram compression technique based on wavelets

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