Computers in Biology and Medicine 37 (2007) 173 – 182 www.intl.elsevierhealth.com/journals/cobm ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds Gülay Tohumoglu ∗ , K. Erbil Sezgin Department of Electrical and Electronics Engineering, University of Gaziantep, 27310 Gaziantep, Turkey Received 16 March 2005; accepted 14 November 2005 Abstract The modified embedded zero-tree wavelet (MEZW) compression algorithm for the one-dimensional signal was originally derived for image compression based on Shapiro’s EZW algorithm. It is revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate. The EZW and MEZW algorithms apply the chosen threshold values or the expressions in order to specify that the significant transformed coefficients are greatly significant. Thus, two different threshold definitions, namely percentage and dyadic thresholds, are used, and they are applied for different wavelet types in biorthogonal and orthogonal classes. In detail, the MEZW and EZW algorithms results are quantitatively compared in terms of the compression ratio (CR) and percentage root mean square difference (PRD). Experiments are carried out on the selected records from the MIT-BIH arrhythmia database and an original ECG signal. It is observed that the MEZW algorithm shows a clear advantage in the CR achieved for a given PRD over the traditional EZW, and it gives better results for the biorthogonal wavelets than the orthogonal wavelets.  2005 Elsevier Ltd. All rights reserved. Keywords: Multi-iteration EZW; ECG signal compression; Wavelets; 1-D signal coding 1. Introduction Wavelet theory is a synthesis of ideas from many different research domains. It provides very general techniques that can be applied to many tasks in signal processing, and therefore has numerous potential applications [1]. The basic idea for wavelets is not new, and it goes back to the Galerkin func- tion [2]. Fourier analysis has a place in signal processing, but wavelet analysis has advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuous and sharp spikes. In other words, wavelets are based on multiresolution analysis, and wavelet decomposition allows analyzing a signal at different resolu- tion levels. This makes wavelets interesting and useful [3,4]. ∗ Corresponding author. Tel.: +90 342 360 12 00 x2109; fax: +90 342 361 11 03. E-mail addresses: g_tohumoglu@gantep.edu.tr (G. Tohumoglu), sezgin@gantep.edu.tr (K.E. Sezgin). 0010-4825/$ - see front matter  2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.compbiomed.2005.11.004 Over the past several years, many different signal com- pression techniques have been devised for the ECG signal to overcome storage problems owing to memory and bandwidth requirements. Existing data compression for ECG signals can be categorized into two groups: direct and transform schemes. Examples of direct schemes that attempt to code the signal di- rectly are FAN, AZTEC, CORTES and ASEC. A good review and comparison of some of these methods are presented in Ref. [5]. The wavelet transform methods among the transform schemes have shown promise of good performance due to their good localization properties in the time and frequency domain [6,7]. In one-dimensional (1D) or 2D signal compression ap- plications, wavelet-based [8–10], referred to as multiresolution analysis, outperform other coding schemes like the one based on discrete cosine transform [11]. Wavelet-based coding is more robust under transmission and decoding errors, and also facilitates progressive transmission of signals. The wavelet- based linear prediction is studied in Ref. [9]. In Hilton’s paper [10], the application of EZW-coding algorithm to eight dif- ferent wavelets and wavelet packets in the large class of the