An ECG Signal Denoising Method Using Conditional Generative Adversarial Net

Generalizability and Clinical Implications of Electrocardiogram Denoising with Cardio-NAFNet

“…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.…”

“…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.…”

“…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].…”

“…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.…”

“…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.…”