Syntactic Pattern Recognition of the ECG
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Biomedical Research
ECG signal denoising and baseline wander correction based on the empirical mode decomposition
Computers in Biology and Medicine
A new mathematical based QRS detector using continuous wavelet transform
Computers and Electrical Engineering
Automatic detection of ECG wave boundaries using empirical mode decomposition
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
QRS complex detection using Empirical Mode Decomposition
Digital Signal Processing
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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.