الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
The electrical activity of the heart, called Electrocardiagram (ECG), is a very important diagnostic parameter for the measurement of the heart activity and the health condition of the patient for the treatment purposes, so for the long term monitoring and analysis of the ECG data, compression techniques are very important and necessary for the record and storage of huge amounts of data signals, as small as possible for the storage with the high accuracy while retaining the important diagnostic clinical parameters in case of reconstruction of the original compressed signal. The objective of this thesis is to compress ECG signals with different advanced methods and techniques. In this thesis two new advanced methods have been developed for the compression of ECG signal. The first technique is based on converting the 1-D ECG into the 2-D array. This preprocessing operation includes detecting the QRS complex, and then alignment and period sorting are used to convert the ECG signal into a matrix. Normalization is performed to scale the values of the matrix and make a gray scale image due to the 2-D ECG. Then, the algorithm applies decimation for ECG compression. The reconstruction of the original ECG signals can be performed using inverse interpolation techniques such as the linear minimum mean square error (LMMSE), the maximum entropy, and the regularization theory. The second technique is based on wavelet transform and SPIHT (set partition in hierarchically tree) code, where using 2D data array of ECG signal and applying 2D wavelet transform and (SPHIT).