Data processing of stress ECGs using discrete cosine transform.

ABSTRACT

The signal processing steps for the analysis of stress ECGs are aimed at improving the signal to noise ratio (SNR) of recordings in addition to eliminating artifacts due to respiration, movement of arms, etc. In this paper, we bring forth two important applications of the discrete cosine transform (DCT) for noise suppression and removal of baseline wander. The noise suppression algorithm has been framed on the basis of a two step procedure involving singular value decomposition (SVD) smoothing operation in transform domain followed by that in time domain. The mean square error (MSE) resulting from the first step is shown to effectively follow the trend obtained by using an ideal Wiener filter using DCT. In the second step, the degree of closeness to the minimum mean square error (MMSE) of the ideal Wiener filter is improved by subjecting the filtered outputs to a second SVD smoothing operation in time domain. Application of this scheme to noisy records has resulted in near perfect reproduction of the original noise free ECG without significant alterations in its morphological features.