Empirical Mode Decomposition (EMD) combined with other algorithms improves the performance of noise cancellation in ECG signal processing.
This review summarizes the application and future challenges of Empirical Mode Decomposition (EMD) techniques for ECG signal denoising, emphasizing that hybrid EMD approaches improve noise cancellation.
Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.
Han et al. (Fri,) conducted a review in Heart diseases (ECG signal processing). Empirical Mode Decomposition (EMD) technique was evaluated. Empirical Mode Decomposition (EMD) combined with other algorithms improves the performance of noise cancellation in ECG signal processing.