Empirical mode decomposition based ECG enhancement and QRS detection
In this paper an Empirical Mode Decomposition (EMD) based ECG signal enhancement and QRS detection algorithm is proposed. Being a non-invasive measurement, ECG is prone to various high and low frequency noises causing baseline wander and power line interference, which act as a source of error in QRS and other feature extraction. EMD is a fully adaptive signal decomposition technique that generates Intrinsic Mode Functions (IMF) as decomposition output. Here, first baseline wander is corrected by selective reconstruction based slope minimization technique from IMFs and then high frequency noise is removed by eliminating a noisy set of lower order IMFs with a statistical peak correction as high frequency noise elimination is accompanied by peak deformation of sharp characteristic waves. Then a set of IMFs are selected that represents QRS region and a nonlinear transformation is done for QRS enhancement. This improves detection accuracy, which is represented in the result section. Thus in this method a single fold processing of each signal is required unlike other conventional techniques.
ecg qrs detection emd baseline wander peak correction power line noise