2 Dimensional Wavelet Transform for Texture and Signal Analysis

2D wavelet transform has been used for texture and signal analysis. An algorithm based on wavelet transform has been developed for detecting ECG characteristic points. This work derives a 2-Dimensional spectrum estimator from some recent results on the statistical properties of wavelet packet coefficients of random processes. Textures are complex visual patterns composed of entities, or sub-patterns that have characteristic brightness, color, slope, size etc. Thus texture can be regarded as a similarity grouping in an image. Wavelet transforms have become appealing alternatives to the Fourier transform for image analysis and processing. The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Generally, the recorded ECG signal is often contaminated by noise. In order to extract useful information from the noisy ECG signals, the raw ECG signals has to be processed. The baseline wandering is significant and can strongly affect ECG signal analysis. The detection of QRS complexes in an ECG signal provides information about the heart rate, the conduction velocity, the condition of tissues within the heart as well as various abnormalities. It supplies evidence for the diagnosis of cardiac diseases. The QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious base line drift and noise.