Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database
Computers and Biomedical Research
Generalized cross validation for wavelet thresholding
Signal Processing
Using the Wavelet Transform for T-wave alternans detection
Mathematical and Computer Modelling: An International Journal
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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The phenomenon of cardiac repolarization or T-wave alternans (TWA) has attracted tremendous attention after its acceptance as a marker of malignant ventricular arrhythmias leading to sudden cardiac death. TWA manifests subtle alternation in the ST-T segment of ECG, therefore, its detection and estimation is considerably affected by deteriorated signal conditions due to noise. In this paper, we evaluate the potential of discrete wavelet transform thresholding for accurate trend estimation of ECG repolarization segment. An exhaustive experimental approach is adopted to find the optimal parameter sets for accurate trend estimation, including mother wavelets, decomposition levels and other common thresholding parameters. Validation study is carried out after shortlisting Coiflet4 and Symlet7 wavelets, subsequently applied to spectral method (SM) and modified moving average method (MMAM) for performance evaluation. For both the TWA analysis schemes, proposed method is inserted within the preprocessing stage after ST-T segmentation of ECG. When using wavelet based thresholding, SM achieves a detection gain of 3 dB in the case of Gaussian and Laplacian noises. The estimation bias and error in Gaussian noise are also improved by 40% and 62.5%, respectively, for SNR=