Blind ECG Restoration by Operational Cycle-GANs

Kıranyaz, Serkan; Devecioğlu, Özer Can; İnce, Türker; Malik, Junaid; Chowdhury, Muhammad E. H.; Hamid, Tahir; Mazhar, Rashid; Khandakar, Amith; Tahir, Anas; Rahman, Tawsifur; Gabbouj, Moncef

doi:10.1109/tbme.2022.3172125

Cited by 26 publications

(13 citation statements)

References 29 publications

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“…From skimage.restoration we utilized the denoise wavelet function with BayesShrink method to estimate the soft wavelet threshold for every single wavelet sub-band with 3 levels of wavelet decomposition of wavelets db4 and sym8. As a representative of newly emerging techniques, we make use of a Cycle-GAN [12]. This method performs a blind ECG restoration by the application of the cycleconsistent generative adversarial networks for noise-free ECG recovery.…”

Section: Preprocessing Methodsmentioning

confidence: 99%

Efficiency of different heartbeat detection methods by using alternative noise reduction algorithms

Vollmer¹,

Guzmán²

2022

Computing in Cardiology Conference (CinC)

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Motivation: ECG noise reduction is an essential step in the ECG preprocessing pipeline. In particular, the quality of beat detection can be affected by several artifacts. The variety of available methods ranges from filter-based techniques (e.g. Butterworth or FIR), signal decomposition (e.g. wavelets) to neural networks (Cycle-GAN).Methods: 12-lead resting ECGs from the SFB/TR19 study on inflammatory dilated cardiomyopathy (n=704) and from the Study of Health in Pomerania (n=17,717) were preprocessed with 14 different methods to evaluate the accuracy of heartbeat detection methods in relation to the chosen method for preprocessing. Open source signal libraries (neurokit2, py-ecg-detectors, WFDB among others) with 34 detectors were evaluated. Sensitivity and Positive Predictive Values (PPV) were computed for each combination of preprocessing and detection method in a train/test scheme. Annotations were corrected for fixed delays and scored at a tolerance of 50 ms. Results and conclusion:Eplimited performed best, regardless of the chosen preprocessing method. For diseased ECGs, in Kalidas2017 it was seen an improvement in performance from 0.713 to 0.876 and from 0.778 to 0.879 for PPV and sensitivity respectively. Best results can be achieved with ECG leads V3, V5 and V6.

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Section: Preprocessing Methodsmentioning

confidence: 99%

Efficiency of different heartbeat detection methods by using alternative noise reduction algorithms

Vollmer¹,

Guzmán²

2022

Computing in Cardiology Conference (CinC)

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“…The outcomes of the introduced method are compared with the results of the state‐of‐the‐art, whose codes are available, 15,20 and 21,22 . The average values of the de‐noising outcomes of all methods and for all volunteers are illustrated in Table 3.…”

Section: Tests and Comparisonsmentioning

confidence: 99%

“…Wavelet-based methods, [13][14][15] PCA-based methods, [16][17][18] Kalman filter-based methods, 19,20 and artificial intelligence-based methods (AI). [21][22][23] The main limitation of the wavelet-based approaches is the difficulty in selecting the appropriate wavelet basis and decomposition level. The wavelet-based filtering methods require choosing a wavelet basis and decomposition level that can affect the de-noising performance.…”

Section: Introductionmentioning

confidence: 99%

“…While conventional filters can effectively eliminate most of these noise types, extracting a clean heartbeat from an ECG signal corrupted with “Additive White Gaussian Noise” (AWGN) remains a critical issue 12 . From the literature, there are four main methods for de‐noising ECG signals: Wavelet‐based methods, 13–15 PCA‐based methods, 16–18 Kalman filter‐based methods, 19,20 and artificial intelligence‐based methods (AI) 21–23 . The main limitation of the wavelet‐based approaches is the difficulty in selecting the appropriate wavelet basis and decomposition level.…”

Section: Introductionmentioning

confidence: 99%

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An efficient wavelet thresholding strategy and robust shrinkage approach for de‐noising ECG signal

Ahmed

2023

Int J Imaging Syst Tech

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This paper introduces an efficient wavelet thresholding strategy and robust shrinkage approach (WTS) for de‐noising the ECG signals. The optimal mother wavelet for de‐noising the ECG signal is automatically selected based on cross‐correlation and energy‐to‐entropy indices. A dynamic threshold is applied to various levels of decomposition to eliminate different types of noise. A shrinkage approach is suggested to attain seamless on–off transitions. The achievement of the WTS is compared with the state of the art reported in the literature using 180 real ECG signals collected from 60 volunteers. The ECG signals were recorded using three standard ECG machines without applying the filtering option to provide a diverse range of noisy ECG signals. The de‐noising outcomes show that the presented WTS can automatically select the optimal mother wavelet for the particular ECG signal, and it is superior to other techniques in terms of the average predicted signal‐to‐noise ratio (), the average predicted root‐mean‐square error (), and the average execution time (AET).

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“…Unlike EEG, both classical and deep Machine Learning techniques have been used to correct motion artifacts from other physiological signals such as photoplethysmography (PPG) [45][46][47][48][49][50][51][52], electrocardiogram (ECG) [45,[53][54][55][56][57][58][59][60], electromyogram (EMG) [61,62], and phonocardiogram (PCG) [63]. To fill this void, this study presents a novel 1D convolutional neural network (CNN)-based signal synthesis or reconstruction approach to correct motion artifacts from motion-corrupted EEG recordings.…”

Section: Introductionmentioning

confidence: 99%

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

Mahmud

Hossain

Chowdhury

et al. 2022

Neural Comput & Applic

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Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. The performance of the proposed model was compared with ten other 1D CNN models: FPN, LinkNet, UNet, UNet+, UNetPP, UNet3+, AttentionUNet, MultiResUNet, DenseInceptionUNet, and AttentionUNet++ in removing motion artifacts from motion-contaminated single-channel EEG signal. All the eleven deep CNN models are trained and tested using a single-channel benchmark EEG dataset containing 23 sets of motion-corrupted and reference ground truth EEG signals from PhysioNet. Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (η). The proposed MLMRS-Net model has shown the best denoising performance, producing an average ΔSNR, η, and MAE values of 26.64 dB, 90.52%, and 0.056, respectively, for all 23 sets of EEG recordings. The results reported using the proposed model outperformed all the existing state-of-the-art techniques in terms of average η improvement.

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Blind ECG Restoration by Operational Cycle-GANs

Cited by 26 publications

References 29 publications

Efficiency of different heartbeat detection methods by using alternative noise reduction algorithms

Efficiency of different heartbeat detection methods by using alternative noise reduction algorithms

An efficient wavelet thresholding strategy and robust shrinkage approach for de‐noising ECG signal

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

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