A method of wavelet-based dual thresholding de-noising for ECG signal

Liu, Yi; Song, Yuqing

doi:10.1109/cisp.2014.7003941

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Patil and Holambe [103 ] used an adaptive method that utilised the level‐dependent minimax rule to denoise the ECG signal. Yi and Song [104 ] demonstrated the use of an innovative thresholding function based on level‐dependent sqtwolog rule to tackle ECG noise with the aid of actual signal situations to switch hard‐ and soft‐thresholding modes. Poornachandra [105 ] has formulated the subband level‐dependent median (S‐median) threshold based on DWT for the recuperation of the ECG signals tainted by the noises.…”

Section: Techniques For Ecg Noise Removalmentioning

confidence: 99%

Review of noise removal techniques in ECG signals

Chatterjee

Thakur

Yadav

et al. 2020

IET signal process.

170

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Section: Techniques For Ecg Noise Removalmentioning

confidence: 99%

Review of noise removal techniques in ECG signals

Chatterjee

Thakur

Yadav

et al. 2020

IET signal process.

170

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“…Patil and Holambe [22] have set the adaptive threshold value in terms of the level-dependent minimax rule base on the DWT for handling different kinds of noises in the ECG signals. Yi and Song [23] have presented the adjustable thresholding function with the level-dependent sqtwolog rule based on the DWT for tackling different kinds of noises in the ECG signals. This thresholding function can be adjusted from the hard to the soft modes according to the actual signal situations.…”

Section: B: Performing Thresholding On the Wavelet Coefficients In A mentioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

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Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of ''wavelets for ECG'' in the future. INDEX TERMS Wavelets for electrocardiogram, signal denoising, wave detection, heartbeat classification, overview and taxonomy. I. OUTLINE AND INTRODUCTION A. OUTLINE AND CONTRIBUTION

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