ECG Data Analysis with Denoising Approach and Customized CNNs

Mishra, Abhinav; Dharahas, Ganapathiraju; Gite, Shilpa; Kotecha, Ketan; Koundal, Deepika; Zaguia, Atef; Kaur, Manjit; Lee, Heung-No

doi:10.3390/s22051928

Cited by 16 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A convolutional neural networks with various ECG filters for arrhythmia detection, demonstrating improved classification accuracy with denoised signals compared to existing methods [16]. From the literature survey, we can conclude that various techniques have been explored for ECG signal denoising and QRS complex detection, including Empirical Mode Decomposition(EMD)., Karhunen-Loève Transform (KLT), wavelet-based approaches, and adaptive filtering methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Emd and Wavelet-Based Ecg Signal Denoising and QRS Complex Detection

Rao

2024

IJSREM

View full text Add to dashboard Cite

This project presents a novel method for noise removal and QRS complex detection in electrocardiogram (ECG) signals, employing Empirical Mode Decomposition (EMD), windowing, and wavelet thresholding techniques. Noise is added to the ECG signal, and EMD is applied to obtain denoised components. Local minima detection and peak matching identify QRS complex boundaries. Windowing isolates the QRS regions, followed by wavelet thresholding for further denoising. Evaluation metrics like improved Signal-to-Noise Ratio, Mean Square Error, and Percent Root Mean Square Difference are calculated at different input SNR values, demonstrating the method's effectiveness in preserving the QRS complex while removing noise.. Keywords: ECG signal processing, QRS complex detection, Empirical Mode Decomposition (EMD), Windowing, Wavelet thresholding, QRS complex extraction, Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Percent Root Mean Square Difference (PRD).

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Emd and Wavelet-Based Ecg Signal Denoising and QRS Complex Detection

Rao

2024

IJSREM

View full text Add to dashboard Cite

show abstract

“…In general, the DWT-based denoising methods could better retain the details of higher frequency signal components compared to traditional filtering-based denoising methods, which rely on discrete Fourier transform [36]. However, the DWT-based denoising methods are offline algorithms that cannot apply to real-time ECG signal denoising [125]. Design for advanced denoising algorithms specifically considering ECG characteristics is needed to be explored.…”

Section: Preprocessingmentioning

confidence: 99%

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

et al. 2023

View full text Add to dashboard Cite

Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

show abstract

“…As shown in (7), to minimize the error entropy, it is sufficient to maximize the information potential. For online training methods used in the adaptive filters, IP should be estimated iteratively.…”

Section: Mee-based Anc Systemmentioning

confidence: 99%

A novel adaptive noise cancellation method based on minimization of error entropy for electrocardiogram denoising

Mousa Saad,

Al-Chaabawi,

H. Hassan

2023

IJEECS

View full text Add to dashboard Cite

<span>In this paper, we use an adaptive method that conforms to the error entropy criterion in order to eliminate noise from cardiac signals electrocardiogram (ECG). In previous works, the mean squared error (MSE) criterion has been used to adaptive noise cancelation of ECG signals, which only has the ability to minimize the second moment of error. The MSE criterion only works optimally on systems with Gaussian noise and stationary signals, so this is not suitable for ECG signals that are non-stationary and have non-Gaussian noise. In contrast, the use of error entropy-based algorithms like minimum error entropy (MEE) is very useful in ECG noise cancelation. The results of the proposed algorithm indicate a significant advantage in terms of signal-to-noise ratio (SNR) and convergence value compared to the algorithm based on MSE criteria.</span>

show abstract

ECG Data Analysis with Denoising Approach and Customized CNNs

Cited by 16 publications

References 45 publications

Emd and Wavelet-Based Ecg Signal Denoising and QRS Complex Detection

Emd and Wavelet-Based Ecg Signal Denoising and QRS Complex Detection

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

A novel adaptive noise cancellation method based on minimization of error entropy for electrocardiogram denoising

Contact Info

Product

Resources

About