ECG De-noising: A Comparison Between EEMD-BLMS and DWT-NN Algorithms.

Electrocardiogram (ECG) is a widely used noninvasive method to study the rhythmic activity of the heart and thereby to detect the abnormalities. However, these signals are often obscured by artifacts from various sources and minimization of these artifacts are of paramount important. This paper proposes two adaptive techniques, namely the EEMD-BLMS (Ensemble Empirical Mode Decomposition in conjunction with the Block Least Mean Square algorithm) and DWT-NN (Discrete Wavelet Transform followed by Neural Network) methods in minimizing the artifacts from recorded ECG signals, and compares their performance. These methods were first compared on two types of simulated noise corrupted ECG signals: Type-I (desired ECG+noise frequencies outside the ECG frequency band) and Type-II (ECG+noise frequencies both inside and outside the ECG frequency band). Subsequently, they were tested on real ECG recordings. Results clearly show that both the methods works equally well when used on Type-I signals. However, on Type-II signals the DWTNN performed better. In the case of real ECG data, though both methods performed similar, the DWT-NN method was a slightly better in terms of minimizing the high frequency artifacts.