DOI: https://doi.org/10.15368/theses.2009.58
Available at: https://digitalcommons.calpoly.edu/theses/101
Date of Award
6-2009
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
Advisor
Xiao-Hua (Helen) Yu
Abstract
Electrocardiogram (ECG) signal processing has been the subject of intense research in the past years, due to its strategic place in the detection of several cardiac pathologies. However, ECG signal is frequently corrupted with different types of noises such as 60Hz power line interference, baseline drift, electrode movement and motion artifact, etc. In this thesis, a hybrid two-stage model based on the combination of wavelet decomposition and artificial neural network is proposed for ECG noise reduction based on excellent localization features: wavelet transform and the adaptive learning ability of neural network. Results from the simulations validate the effectiveness of this proposed method. Simulation results on actual ECG signals from MIT-BIH arrhythmia database [30] show this approach yields improvement over the un-filtered signal in terms of signal-to-noise ratio (SNR).