Arrhythmia identification and classification using wavelet centered methodology in ECG signals

Arumugam, Maheswari; Sangaiah, Arun Kumar

doi:10.1002/cpe.5553

Cited by 16 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The “Haar” wavelets were chosen in this study as they are efficient and are commonly used in several studies analyzing ECG data [ 56 , [78] , [79] , [80] , [81] , [82] ]. The number of decomposition levels is 3 as it showed excellent performance in various studies analyzing ECG data [ [83] , [84] , [85] , [86] ]. The approximation coefficients are used as the authors of the study [ 87 ] concluded that the approximation coefficients extracted from medical signals have demonstrated superior performance compared to details coefficients.…”

Section: Methodsmentioning

confidence: 99%

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

Attallah

2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) application, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.

show abstract

Section: Methodsmentioning

confidence: 99%

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

Attallah

2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…From several case studies that are often examined in research in the case of ECG and wavelets mostly based on the paper obtained that is related to Arrhythmia. From studies using arrhythmia case studies in general there are 10 studies [12][13], [16][17], [21], [25][26], [28], [38], [43]. Atrial fibrilation mentioned specifically there are four studies [27] [36], [46], [48].…”

Section: Fig 3 Number Of Articles By Yearmentioning

confidence: 99%

“…Some even do not go through the pre-processing stage and go directly to the characteristic extract. Wavelet methods used in the preprocessing phase based on the journals used are Wavelet Transform [12], [42], [47], Wavelet Decomposition [14], Discrete Wavelet Transform-DWT [19], [22], [34][35][36][37], [43][44], [1], Transform-WPT Package Wavelet [46]. As for pre-processing that does not use wavelets and uses other methods (Table I).…”

Section: A Pre-processingmentioning

confidence: 99%

See 1 more Smart Citation

Use of Wavelets in Electrocardiogram Research: a Literature Review

Darwan¹,

Mustafidah

2021

Jurnal Informatika

View full text Add to dashboard Cite

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.

show abstract

“…ECG signals are widely used in the diagnosis of heart disease and are commonly regarded as the most important tool for heart disease diagnosis in clinical practice [1]. Therefore, the analysis and processing of ECG signals is of great significance for heart disease detection.…”

Section: Introductionmentioning

confidence: 99%

An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

Zhang

Wang

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

show abstract

Arrhythmia identification and classification using wavelet centered methodology in ECG signals

Cited by 16 publications

References 29 publications

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

Use of Wavelets in Electrocardiogram Research: a Literature Review

An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

Contact Info

Product

Resources

About