Various ECG denoising techniques were evaluated, identifying specific methods like Wavelet-VBE, GAN1, and DLSR as optimal for removing specific noises such as Gaussian noise and muscle artifacts.
This review categorizes and compares state-of-the-art ECG denoising methods, identifying the most effective algorithms for specific types of signal noise and artifacts.
An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre‐processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state‐of‐the‐art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root‐mean‐square error, percentage‐root‐mean‐square difference, and signal‐to‐noise ratio improvement, thus comparing various ECG denoising techniques on MIT‐BIH databases, PTB, QT, and other databases. It is observed that Wavelet‐VBE, EMD‐MAF, GAN2, GSSSA, new MP‐EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP‐EKF, DLSR, and AKF perform comparatively well. For base‐line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power‐line interference removal, DLSR and EWT perform well. Finally, FCN‐based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.
Chatterjee et al. (Tue,) conducted a review in ECG signal noise. ECG denoising techniques was evaluated on Denoising performance (RMSE, PRD, SNR improvement). Various ECG denoising techniques were evaluated, identifying specific methods like Wavelet-VBE, GAN1, and DLSR as optimal for removing specific noises such as Gaussian noise and muscle artifacts.