Computerized electrocardiogram (ECG), electroencephalogram (EEG), and magnetoencephalogram (MEG) processing systems have been widely used in clinical practice [1] and they are capable of recording and processing long records of biomedical signals. The use of such systems (1) enables the construction of large signal databases for subsequent evaluation and comparison, (2) makes the transmission of biomedical information feasible over telecommunication networks in real time or off line, and (3) increases the capabilities of ambulatory recording systems such as the Holter recorders for ECG signals. In spite of the great advances in VLSI memory technology, the amount of data generated by digital systems may become excessive quickly. For example, a Holter recorder needs more than 200 Mbits/day of memory space to store a dual-channel ECG signal sampled at a rate of 200 samples/sec with 10 bit/sample resolution. Since the recorded data samples are correlated with each other, there is an inherent redundancy in most biomedical signals. This can be exploited by the use of data compression techniques which have been successfully utilized in speech, image, and video signals [2] as well.