Signal enhancement of wearable ECG monitoring sensors based on Ensemble Empirical Mode Decomposition
The use of electrocardiogram (ECG) signals is an important standard for the diagnosis of heart diseases and other pathological phenomena. The ECG signal, however, is always contaminated by different types of noise, especially when the sensor is worn by patients during their normal activities, where the muscle and motion artefact are the dominant noise. This paper proposes a novel ECG enhancement method, which is based on Ensemble Empirical Mode Decomposition, to eliminate the contact noise in the signals. The performance of the proposed method is validated by using real data from the MIT-BIH database. Simulation results show that ECG signals from wearable monitoring sensors can be significantly enhanced by filtering out the contact noise while keeping all of the ECG features. The EEMD-based method exhibits obvious advantages over other similar ones in terms of de-noising.
|Keywords||Electrocardiogram, Ensemble Empirical Mode Decomposition, Gaussian noise, motion artefact, muscle artefact, wearable medical monitoring sensor|
|Conference||2011 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2011|
He, X. (Xiaochuan), Goubran, R, & Liu, P. (2011). Signal enhancement of wearable ECG monitoring sensors based on Ensemble Empirical Mode Decomposition. In MeMeA 2011 - 2011 IEEE International Symposium on Medical Measurements and Applications, Proceedings. doi:10.1109/MeMeA.2011.5966752