A wavelet-based method for power-line interference removal in ECG signals

Oliveira, Bruno Rodrigues de; Duarte, Marco Aparecido Queiroz; Abreu, Caio Cesar Enside de; Filho, Jozué Vieira

doi:10.1590/2446-4740.01817

Cited by 28 publications

(9 citation statements)

References 31 publications

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“…Related literature review of this study almost 67 % paper used all using wavelet at the extraction stage of its feature. With the use of Discrete Wavelet Transform-DWT method consists of 50% journals used using such methods include: [15], [18], [20], [24][25], [28][29][30][31][32], [35], [38], and [41]. Continuous Wavelet Transform (CWT) include: [17], [21], [23], and [39][40].…”

Section: B Feature Extractionmentioning

confidence: 99%

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Use of Wavelets in Electrocardiogram Research: a Literature Review

Darwan¹,

Mustafidah

2021

Jurnal Informatika

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Currently the introduction and detection of heart abnormalities using electrocardiogram (ECG) is very much. ECG conducted many research approaches in various methods, one of which is wavelet. This article aims to explain the trends of ECG research using wavelet approach in the last ten years. We reviewed journals with the keyword title "ecg wavelet" and published from 2011 to 2020. Articles classified by the most frequently discussed topics include: datasets, case studies, pre-processing, feature extraction and classification/identification methods. The increase in the number of ECG-related articles in recent years is still growing in new ways and methods. This study is very interesting because only a few researchers focus on researching about it. Several approaches from many researchers are used to obtain the best results, both by using machine learning and deep learning. This article will provide further explanation of the most widely used algorithms against ECG research with wavelet approaches. At the end of this article it is also shown that the critical aspect of ECG research can be done in the future is the use of datasets, as well as the extraction of characteristics and classifications by looking at the level of accuracy.

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Section: B Feature Extractionmentioning

confidence: 99%

“…Wavelet Packet Decomposition (WPD): [33], [43], and [48]. Wavelet Packet Transform (WPT): [26], and Wavelet Reconstruction [41]. Some researchers are using methods other than Wavelet at the extraction stage of the feature as seen in Table II.…”

Section: B Feature Extractionmentioning

confidence: 99%

Use of Wavelets in Electrocardiogram Research: a Literature Review

Darwan¹,

Mustafidah

2021

Jurnal Informatika

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“…In general, the frequency of BW is in the range of 0–0.8 Hz [5]. The PLI is characterised by the fundamental frequency of 50 Hz and amplitude‐reduced multiple higher harmonics at 100 Hz, 150 Hz with varying phase [29]. The BW and PLI can be localised in the frequency domain [5].…”

Section: Proposed Hybrid Ecg Enhancement Schemementioning

confidence: 99%

Hybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation

Rakshit

Das

2019

IET Science, Measurement & Technology

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“…In the same context, an adaptive Kalman filter is used by Vullings et al (2011) to improve the ECG signal quality. Chang and Liu (2011), for their part, used the Wiener filter to eliminate Gaussian white noise in the ECG signal, whereas Oliveira et al (2018) explored removal of power-line interference by zeroing some wavelet scales.…”

Section: Introductionmentioning

confidence: 99%

ECG signal denoising by fractional wavelet transform thresholding

2020

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Introduction The analysis of electrocardiogram (ECG) signals allows experts to diagnose several cardiac disorders. However, the accuracy of such diagnosis depends heavily on the signal quality. In this paper, an efficient method based on fractional wavelet decomposition coupled with thresholding techniques is proposed for noise removal. Methods The usual low-pass and high-pass filters of the wavelet transform are replaced by fractional-order ones. Thus, fractional wavelets are proposed, simulated, and compared to other wavelets for ECG denoising. The denoising process was made operational by the means of an appropriate choice of the wavelet transform coefficient thresholding and the wavelet decomposition level of the signal. Results Considering the relative error metrics, the best wavelet function for efficient denoising is the fractional one. In our study, we have used eight real ECG signals from the Physionet MITBIH. In order to prove the effectiveness of our method, we investigated the filtering of two types of noises, namely Gaussian white noise and power-line interference (PLI) noise. The proposed method removed the Gaussian white noise completely and had better performance on the PLI noise. Considering classical metrics of assessment, results show the advantage of the proposed method compared to other types of wavelets. Conclusion The proposed method is the most suitable one for removing PLI and Gaussian white noise from ECG signals with superior performance than other wavelets. Also, it can be applied for high-frequency denoising even without a priori frequency knowledge.

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A wavelet-based method for power-line interference removal in ECG signals

Cited by 28 publications

References 31 publications

Use of Wavelets in Electrocardiogram Research: a Literature Review

Use of Wavelets in Electrocardiogram Research: a Literature Review

Hybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation

ECG signal denoising by fractional wavelet transform thresholding

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