Discrete Wavelet Transform in Compression and Filtering of Biomedical Signals

Biomedical signals are a kind of signals that are measured from a specific part of the body, for example from the hearth (electrocardiography: ECG), muscles (electromyography: EMG) and brain (electroencephalography: EEG). This kind of signals have a no-stationary behavior, it means the behavior through the time is changing every time window. For this reason, the pre-processing, processing, and analysis should be different of the deterministic and stationary signals. One of the methods used in the last years to examine biomedical signals is the Discrete Wavelet Transform (DWT), it represents both time and frequency the signal’s characteristics in a multi-resolution mode. In this chapter, we are going to present two applications of the DWT in biomedical signals, it known as filtering and compression. When you have a device that measures the body’s signals, it is desired that the information stored or transmitted have high quality and low redundancy; this corresponds to apply a filter and compress the signal. These two blocks (filtering and compression) are added once the signal is acquired and processed by digital signal processing methods. The goal of using the DWT in an algorithm of filtering and compression biomedical signals is the possibility of choosing the signal’s coefficients with a significant energy and discards the others that have a very low percentage of all energy. This is possible because in every level of decomposition, the energy of different frequencies and time position is related to a specific coefficient. In the first part, we present one model of filtering of biomedical signals based on Discrete Wavelet Transform. We analyze the different parameters in the model and its relation to the quality of the new signal. Every parameter affects in low or high manner the quality of the filtered signal and we present the most common test to probe the signal's distortion when the coefficients with low energy have been removed. Additionally, we present some results with one real EMG signal with different configuration of the parameters. In the second part, we extend the model of filtering to include the stage of compression; we explain the encoding block, which is added to the compression model. Two lossless encoding methods are explained and compared. The compression of some records of ECG is presented.