Characterising the discrete wavelet transform of an ECG signal with simple parameters for use in automated diagnosis

The spectral distribution of energy varies between normal ECGs and those from patients post infarct or with ventricular hypertrophies. This suggests that discriminating between normal and abnormal conditions may be possible on the basis of differences in the distribution of spectral energy. The authors compare a reduced Discrete Wavelet Transform characterisation of an ECG QRS complex using three different wavelets. The wavelet transforms are based on dyadic scales and decompose the ECG signals into four detail levels and one approximation level with each decomposition being characterised by a mean and a standard deviation value. The authors' results indicate that, even after reducing the information in each level of decomposition of the wavelet transform to these two simple values, the discriminating power between normal and abnormal cases, calculated using receiver operator curve (ROC) analysis, exceeds 75%. This improves on the results obtained for scalar parameters such as QRS duration, areas and cardiac axis.