8 ECG Signal Compression Using Discrete Wavelet Transform

Transmission techniques of biomedical signals through communication channels are currently an important issue in many applications related to clinical practice. These techniques can allow experts to make a remote assessment of the information carried by the signals, in a very cost-effective way. However, in many situations this process leads to a large volume of information. The necessity of efficient data compression methods for biomedical signals is currently widely recognized. This chapter introduces the compression of ElectroCardioGram (ECG or EKG) signals using Discrete Wavelet Transform (DWT). It is well known that modern clinical systems require the storage, processing and transmission of large quantities of ECG signals. ECG signals are collected both over long periods of time and at high resolution. This creates substantial volumes of data for storage and transmission. Data compression seeks to reduce the number of bits of information required to store or transmit digitized ECG signals without significant loss of signal quality. Although storage space is currently relatively cheap, electronic ECG archives could easily become extremely large and expensive. Moreover, sending ECG recordings through mobile networks would benefit from low bandwidth demands. ECG signal compression attracted considerable attention over the last decade. Several examples of ECG compression algorithms have been described in the literature with compression ratios ranging approximately from 2:1 up to 50:1 (Jalaleddine et al., 1990; Addison, 2005). The main goal here is to provide an up-to-date introduction to this fascinating field; through presenting some of the latest algorithmic innovations and to stimulate readers to investigate the subject in greater depth using the extensive set of references provided (Addison, 2005; Padma et al., 2009). Section 2 introduces the production of the ECG signal and its main timeand frequency-domain parameters. Different ECG signal compression techniques including direct, transformed and optimization methods are presented in section 3. Section 4 discusses the fundamentals of DWTs and their filter bank realizations. Subjective and objective performance measures of compression algorithms are explained in section 5. In section 6, DWT based ECG signal compression algorithms are presented. This includes optimization-based, SPIHT, 2-D, hybrid, and linear prediction based algorithms. Thresholding, and coding of DWT coefficients considering energy packing efficiency and binary significant map are discussed in sections 7 and 8 respectively.