Smoothening and Segmentation of ECG Signals Using Total Variation Denoising –Minimization-Majorization and Bottom-Up Approach

Yadav, Om Prakash; Ray, Shashwati

doi:10.1016/j.procs.2016.05.195

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…This method is a recursive algorithm which segments the original signal into large number of small length segments. These small segments then consequently combined into more significant segments until the stopping criteria is achieved [24]. In order to improve estimation results of Lagrange–Chebyshev polynomial, the segmentation of the signal has to be done before interpolation.…”

Section: Methods and Resultsmentioning

confidence: 99%

Electrocardiogram estimation using Lagrange interpolation

Yadav

Sahu²

2021

Electronics Letters

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An electrocardiogram records activity of cardiac which is collected through the electrodes positioned on specific locations on the human body. These signals are required for cardiac‐related issues. Electrocardiogram signals are quasi‐stable signals and are often contaminated by artefacts. The magnitude and frequency of these artefacts are different, and thus their effect on electrocardiogram is also variable. These artefacts must reduce to a significantly optimum level to make them interpretable. This paper proposes an algorithm to estimate electrocardiogram signals from the MIT‐BIH database signal using Lagrange interpolation through Chebyshev nodes. The performance evaluation of the proposed method has been assessed through standard estimation tools. Performance parameters revealed that the algorithm is suitable for signal estimation irrespective of the characteristic of artefact introduced to the signal.

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Section: Methods and Resultsmentioning

confidence: 99%

Electrocardiogram estimation using Lagrange interpolation

Yadav

Sahu²

2021

Electronics Letters

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“…ECG Signal Segmentation The aim of ECG segmentation is to divide a signal into many parts with similar statistical properties, such as amplitude, nodes, and frequency. The presence, time, and length of each segment of an ECG signal have diagnostic and biophysical significance, and the various sections of an ECG signal have distinctive physiological meaning ( Yadav & Ray, 2016 ). ECG signal segmentation may also be accurately analyzed.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification

Darmawahyuni

Nurmaini

Rachmatullah

et al. 2022

PeerJ Computer Science

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Background Electrocardiogram (ECG) signal classification plays a critical role in the automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be recognized by a human interpreter, they can be detected with precision using artificial intelligence approaches, making the ECG a powerful non-invasive biomarker. However, performing rapid and accurate ECG signal classification is difficult due to the low amplitude, complexity, and non-linearity. The widely-available deep learning (DL) method we propose has presented an opportunity to substantially improve the accuracy of automated ECG classification analysis using rhythm or beat features. Unfortunately, a comprehensive and general evaluation of the specific DL architecture for ECG analysis across a wide variety of rhythm and beat features has not been previously reported. Some previous studies have been concerned with detecting ECG class abnormalities only through rhythm or beat features separately. Methods This study proposes a single architecture based on the DL method with one-dimensional convolutional neural network (1D-CNN) architecture, to automatically classify 24 patterns of ECG signals through both rhythm and beat. To validate the proposed model, five databases which consisted of nine-class of ECG-base rhythm and 15-class of ECG-based beat were used in this study. The proposed DL network was applied and studied with varying datasets with different frequency samplings in intra and inter-patient scheme. Results Using a 10-fold cross-validation scheme, the performance results had an accuracy of 99.98%, a sensitivity of 99.90%, a specificity of 99.89%, a precision of 99.90%, and an F1-score of 99.99% for ECG rhythm classification. Additionally, for ECG beat classification, the model obtained an accuracy of 99.87%, a sensitivity of 96.97%, a specificity of 99.89%, a precision of 92.23%, and an F1-score of 94.39%. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating heart abnormalities between different ECG rhythm and beat assessments by using one outstanding proposed DL architecture.

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“…This is done by implementing Bottom up approach. The Bottom up algorithm, also called as iterative merge which begins by dividing the original time series data of length , into a large number of segments and is consequently merged into bigger segments until stopping criteria is met [33] . So, segmentation is done before interpolation.…”

Section: Transform the Chebyshev Nodes On The Domainmentioning

confidence: 99%

Approximation of ECG Signals Using Chebyshev Nodes and Lagrange-interpolation

Yadav¹,

Ray²

2019

Am. J. Biomed. Sci.

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An ECG (Electrocardiography) is a graphical representation of electrical behavior of heart and is measured by placing electrodes on specific locations of limbs and chest. The characteristics of these signals change during acquisition and transmission due to addition of noises of variable frequency and amplitude. These noises need to be removed for better clinical evaluation. In this paper, ECG signals of MIT-BIH database have been approximated through a series of steps, i.e., denoising using total variation, segmentation using Bottom Up approach and finally utilizing Chebyshev nodes for Lagrange interpolation method. This paper also explores the concept and the benefit of second difference total variation over the first difference. The performance of the method is analyzed in terms of mean absolute deviation, root mean square deviation, percentage root mean square difference error, signal to noise ratio and cross correlation. The results obtained are found to be better than exiting techniques.

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Smoothening and Segmentation of ECG Signals Using Total Variation Denoising –Minimization-Majorization and Bottom-Up Approach

Cited by 13 publications

References 11 publications

Electrocardiogram estimation using Lagrange interpolation

Electrocardiogram estimation using Lagrange interpolation

Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification

Approximation of ECG Signals Using Chebyshev Nodes and Lagrange-interpolation

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