ECG signals denoising using optimized threshold function using discrete Wavelet transform

Madan, Parul; Singh, Vijay Kumar; Singh, Devesh Pratap; Pant, Bhaskar; Diwakar, Manoj

doi:10.1063/5.0113586

AIP Conference Proceedings

2023

DOI: 10.1063/5.0113586

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ECG signals denoising using optimized threshold function using discrete Wavelet transform

Parul Madan¹,

Vijay Kumar Singh²,

Devesh Pratap Singh³

et al.

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Cited by 2 publications

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Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding

Mohguen

2023

Biomedical Engineering / Biomedizinische Technik

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Objectives Novel noise reduction and QRS detection algorithms in Electrocardiogram (ECG) signal based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and the Modified Sigmoid Thresholding Function (MSTF) are proposed in this paper. Methods EMD and EEMD algorithms are used to decompose the noisy ECG signal into series of Intrinsic Mode Functions (IMFs). Then, these IMFs are thresholded by the MSTF for reduction of noises and preservation of QRS complexes. After that, the thresholded IMFs are used to obtain the clean ECG signal. The characteristic points P, Q, R, S and T peaks are detected using peak detection algorithm. Results The proposed methods are validated through experiments on the MIT-BIH arrhythmia database and Additive White Gaussian Noise (AWGN) is added to the clean ECG signal at different input SNR (SNR in). Standard performance parameters output SNR (SNR out), mean square error (MSE), root mean square error (RMSE), SNR improvement (SNR imp) and percentage root mean square difference (PRD) are employed for evaluation of the efficacy of the proposed methods. The results showed that the proposed methods provide significant quantitative and qualitative improvements in denoising performance, compared with existing state-of-the-art methods such as wavelet denoising, conventional EMD (EMD-Conv), conventional EEMD (EEMD-Conv, Stockwell Transform (ST) and Complete EEMD with Adaptative Noise with hybrid interval thresholding and higher order statistic to select relevant modes (CEEMDAN-HIT). Conclusions A detail quantitative analysis demonstrate that for abnormal ECG records 207 m and 214 m at input SNR of −2 dB the SNR imp value is 12.22 and 11.58 dB respectively, which indicates that the proposed algorithm can be used as an effective tool for denoising of ECG signals.

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Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding

Mohguen

2023

Biomedical Engineering / Biomedizinische Technik

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ECG Arrhythmia Measurement and Classification for Portable Monitoring

P Ajitha Gladis,

Ahilan,

Muthukumaran

et al. 2024

Measurement Science Review

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Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired Dingo Optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively.

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ECG signals denoising using optimized threshold function using discrete Wavelet transform

Cited by 2 publications

References 36 publications

Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding

Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding

ECG Arrhythmia Measurement and Classification for Portable Monitoring

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