An ECG Signal Denoising Method Using Conditional Generative Adversarial Net

Wang, Xiaoyu; Chen, Bingchu; Zeng, Ming; Wang, Yuli; Liu, Hui; Liu, Ruixia; Lan, Tian; Xiao-shan, LU

doi:10.1109/jbhi.2022.3169325

Cited by 31 publications

(14 citation statements)

References 31 publications

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“…Table 1 demonstrates that Cardio-NAFNet’s performance has a significant improvement in all noise combinations at all noise levels, resulting in a combined average difference of 11.76dB. In the supplement, we also provide results to compare the results with not only CGAN, but also with Improved denoising autoencoder[13], and adversarial method[21, 28, 29]. We note that while our model follows the general autoencoder architecture, the skip connections from the encoder to the decoder and utilizing NAFBlocks instead of ConvBlocks provide a significant improvement in results.…”

Section: Resultsmentioning

confidence: 99%

“…In the first experiment, we created an identical environment to that of CAE-CGAN's experiment to provide a direct comparison of Cardio-NAFNet's performance to CAE-CGAN's performance [21]. Table 1 demonstrates that Cardio-NAFNet's performance has a significant improvement in all noise combinations at all noise levels, resulting in a combined average difference of 11.76dB.…”

Section: Resultsmentioning

confidence: 99%

“…The first experiment's objective is to evaluate Cardio-Net's performance against that of the current SOTA model (CAE-CGAN) [21] in an identical testing environment. We prepared 10 records from Physionet's arrhythmia database [24].…”

Section: Methods Experiments Designmentioning

confidence: 99%

“…The three noise records are baseline wander (BW), muscle artifact (MA), and electrode motion (EM) artifact. To evaluate the denoising capabilities of our model in comparison with the results in CAE-CGAN [21], we created 42 different scenarios of denoising which are combinations of the three noise types (EM, BW, MA, EM+BW, MA+BW, EM+MA, EM+MA+BW) and varying levels of signal to noise ratio (SNR) from 0dB to 5dB.…”

Section: Study Datamentioning

confidence: 99%

“…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]. Upon our review, CAE-CGAN's methods were deemed the most sound as it demonstrated promising improvement in SNR across individual and mixture of noise at varying SNR while also proving that the denoised signal also enhances classification accuracy for each denoised beat.…”

Section: Introductionmentioning

confidence: 99%

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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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