Electrocardiogram (ECG) is a widely used non-invasive method to study the rhythmic activity of theheart. These signals, however, are often obscured by artifacts/noises from various sources and mini-mization of these artifacts is of paramount importance for detecting anomalies. This paper presents athorough analysis of the performance of two hybrid signal processing schemes ((i) Ensemble EmpiricalMode Decomposition (EEMD) based method in conjunction with the Block Least Mean Square (BLMS)adaptive algorithm (EEMD-BLMS), and (ii) Discrete Wavelet Transform (DWT) combined with the Neu-ral Network (NN), named the Wavelet NN (WNN)) for denoising the ECG signals. These methods arecompared to the conventional EMD (C-EMD), C-EEMD, EEMD-LMS as well as the DWT thresholding(DWT-Th) based methods through extensive simulation studies on real as well as noise corrupted ECGsignals. Results clearly show the superiority of the proposed methods.
- Electrocardiogram (ECG)
- Ensemble empirical mode decomposition(EEMD)
- Block least mean square (BLMS)
- Discrete Wavelet Transform (DWT)
- Neural Networks (NN)