ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network
This work addresses noise removal in ECG signals for medical diagnostics, representing an incremental improvement over existing methods.
The authors tackled the problem of noise in ECG signals, which degrades diagnostic utility, by proposing ECGDeDRDNet, a deep learning-based method that achieved superior performance on the MIT-BIH dataset in terms of PSNR, SSIM, SNR, and RMSE compared to conventional and classical techniques.
Electrocardiogram (ECG) signals are frequently corrupted by noise, such as baseline wander (BW), muscle artifacts (MA), and electrode motion (EM), which significantly degrade their diagnostic utility. To address this issue, we propose ECGDeDRDNet, a deep learning-based ECG Denoising framework leveraging a Double Recurrent Dense Network architecture. In contrast to traditional approaches, we introduce a double recurrent scheme to enhance information reuse from both ECG waveforms and the estimated clean image. For ECG waveform processing, our basic model employs LSTM layers cascaded with DenseNet blocks. The estimated clean ECG image, obtained by subtracting predicted noise components from the noisy input, is iteratively fed back into the model. This dual recurrent architecture enables comprehensive utilization of both temporal waveform features and spatial image details, leading to more effective noise suppression. Experimental results on the MIT-BIH dataset demonstrate that our method achieves superior performance compared to conventional image denoising methods in terms of PSNR and SSIM while also surpassing classical ECG denoising techniques in both SNR and RMSE.