An ECG Denoising Method Based on the Generative Adversarial Residual Network

Xu, Bingxin; Liu, Ruixia; Shu, Minglei; Shang, Xiaoyi; Wang, Yinglong

doi:10.1155/2021/5527904

Cited by 17 publications

(15 citation statements)

References 38 publications

(44 reference statements)

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“…In recent years, some researchers have employed machine learning techniques for ECG denoising. Their works can be roughly classified into two categories: (1) improving existing algorithms by machine learning methods for parameter optimization [13,14] and (2) building the ECG denoising model to decompose and reconstruct the ECG using a neural network [15][16][17][18][19]. To reduce baseline drift, Sun et al proposed combining error backpropagation neural network and VMD technique [13].…”

Section: Introductionmentioning

confidence: 99%

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Deep Convolutional Generative Adversarial Network with LSTM for ECG Denoising

Wang

Zhang

et al. 2023

Computational and Mathematical Methods in Medicine

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The electrocardiogram (ECG), as an essential basis for the diagnosis of cardiovascular diseases, is usually disturbed by various noise. To obtain accurate human physiological information from ECG, the denoising and reconstruction of ECG are critical. In this paper, we proposed an ECG denoising method referred to as LSTM-DCGAN which is based on an improved generative adversarial network (GAN). The overall network structure is composed of multiple layers of convolutional networks. Furthermore, the convolutional features can be connected to their time series order dependence by adding LSTM layers after each convolutional layer. To verify the effectiveness and the denoising performance of the improved network structure, we test the proposed algorithm on the famous MIT-BIH Arrhythmia Database with different levels of noise from the MIT-BIH Noise Stress Test Database. Experimental results show that our method can remove the single noise and the mixed noise while retaining the complete ECG information. For the mixed noise removal, the average SNRimp, RMSE, and PRD are 19.254 dB, 0.028, and 10.350, respectively. Compared with the state-of-the-art methods, DCGAN, and the LSTM-GAN methods, our method obtains the higher SNRimp and the lower RMSE and PRD scores. These results suggest that the proposed LSTM-DCGAN approach has a significant advantage for ECG processing and application in complex scenes.

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Section: Introductionmentioning

confidence: 99%

“…However, using DNN is more likely to cause QRS wave group distortion [16]. Therefore, some researchers achieved the denoising model using the residual network structure in GAN [19]. Additionally, the GAN with a CNN-based discriminator and an LSTM-based generator is used to generate ECG [20].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Generative Adversarial Network with LSTM for ECG Denoising

Wang

Zhang

et al. 2023

Computational and Mathematical Methods in Medicine

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“…Recently, breakthroughs have been made across many domains by using deep learning models, including applications in healthcare (Esteva et al, 2019;Parvaneh et al, 2019;Fotiadou et al, 2021;Yang et al, 2021). Deep learning has also attracted some research studies in ECG signal denoising with denoising autoencoders (DAEs) and generative adversarial networks (GANs) being the most representative methods (Xiong et al, 2016;Chiang et al, 2019;Singh and Pradhan, 2021;Xu et al, 2021). Despite the clear potential of these deep learning models, their success often largely depends on the amount and quality of available data to train the models.…”

Section: Introductionmentioning

confidence: 99%

Deep unfolding for multi-measurement vector convolutional sparse coding to denoise unobtrusive electrocardiography signals

Fotiadou

Melaet

Vullings

2022

Front. Signal Process.

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The use of wearable technology for monitoring a person’s health status is becoming increasingly more popular. Unfortunately, this technology typically suffers from low-quality measurement data, making the acquisition of, for instance, the heart rate based on electrocardiography data from non-adhesive sensors challenging. Such sensors are prone to motion artifacts and hence the electrocardiogram (ECG) measurements require signal processing to enhance their quality and enable detection of the heart rate. Over the last years, considerable progress has been made in the use of deep neural networks for many signal processing challenges. Yet, for healthcare applications their success is limited because the required large datasets to train these networks are typically not available. In this paper we propose a method to embed prior knowledge about the measurement data and problem statement in the network architecture to make it more data efficient. Our proposed method aims to enhance the quality of ECG signals by describing ECG signals from the perspective of a multi-measurement vector convolutional sparse coding model and use a deep unfolded neural network architecture to learn the model parameters. The sparse coding problem was solved using the Alternation Direction Method of Multipliers. Our method was evaluated by denoising ECG signals, that were corrupted by adding noise to clean ECG signals, and subsequently detecting the heart beats from the denoised data and compare these to the heartbeats and derived heartrate variability features detected in the clean ECG signals. This evaluation demonstrated an improved in the signal-to-noise ratio (SNR) improvement ranging from 17 to 27 dB and an improvement in heart rate detection (i.e. F1 score) ranging between 0 and 50%, where the range depends on the SNR of the input signals. The performance of the method was compared to that of a denoising encoder-decoder neural network and a wavelet-based denoising method, showing equivalent and better performance, respectively.

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“…Pratik et al proposed a GAN framework that contains convolution layers in its generator and discriminator [20], but the model was only tested to prove its applications with individual types of noise, not any mixtures at varying magnitude. Xu et al utilized ResNet based GAN model but has demonstrated that that the model's denoising capabilities diminished with larger noise samples at lower signal to noise ratio [17]. Wang et al proposed a conditional generative adversarial network (CAE-CGAN) framework where they utilize a convolutional U-Net architecture as a generator, a discriminator with least squared loss, and a pretrained support vector machine (SVM) based classifier that learns to classify each beat [21].…”

Section: Introductionmentioning

confidence: 99%

“…Kabir et al suggested an approach based on noise reduction algorithms in EMD and discrete wavelet transform domains, but the method is also limited to noise reduction with jitters and baseline wandering [12]. The recent advances in deep learning has impacted how ECG signals are processed, through new deep learning models such as autoencoders [13,14], long short-term memory (LSTM) [15], generative adversarial network (GAN) [16,17]. For example, Xiong et al utilized a combination of wavelet transform to deconstruct the signals and deep autoencoders (DAE) [18] to enhance the quality of corrupted signals.…”

Section: Introductionmentioning

confidence: 99%

Generalizability and Clinical Implications of Electrocardiogram Denoising with Cardio-NAFNet

Lim

Chung

Hamm

et al. 2022

Preprint

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The rise of mobile electrocardiogram (ECG) devices came with the rise of frequent large magnitudes of noise in their recordings. Several artificial intelligence (AI) models have had great success in denoising, but the model's generalizability and enhancement in clinical interpretability are still questionable. We propose Cardio-NAFNet, a novel AI-based approach to ECG denoising by employing a modified version of the Non-Linear Activation Free Network (NAFNET). We conducted three experiments for quantitative and qualitative evaluation of denoising, clinical implications, and generalizability. In the first experiment, Cardio-NAFNet achieved a 53.74dB average signal-to-noise ratio across varying magnitudes of noise in beat-to-beat denoising, which is a significant improvement over the current state-of-the-art model in ECG denoising. In the second experiment, we tested the enhancement in clinical interpretation of the ECG signals by utilizing a pre-trained ECG classifier using 8-second long noise-free ECG signals. When the classifier was tested using noisy ECG signals and their denoised counterparts, Cardio-NAFNet's denoised signals provided a 26% boost in classification results. Lastly, we provide an external validation dataset composed of single-lead mobile ECG signals along with signal quality evaluation from physician experts. Our paper suggests a settling method to capture and reconstruct critical features of ECG signals not only in terms of quantitative evaluation but also through generalizable qualitative evaluation.

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An ECG Denoising Method Based on the Generative Adversarial Residual Network

Cited by 17 publications

References 38 publications

Deep Convolutional Generative Adversarial Network with LSTM for ECG Denoising

Deep Convolutional Generative Adversarial Network with LSTM for ECG Denoising

Deep unfolding for multi-measurement vector convolutional sparse coding to denoise unobtrusive electrocardiography signals

Generalizability and Clinical Implications of Electrocardiogram Denoising with Cardio-NAFNet

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