A Simple Method for Guaranteeing ECG Quality in Real-Time Wavelet Lossy Coding

Guaranteeing ECG signal quality in wavelet lossy compression methods is essential for clinical acceptability of reconstructed signals. In this paper, we present a simple and efficient method for guaranteeing reconstruction quality measured using the new distortion index wavelet weighted PRD (WWPRD), which reflects in a more accurate way the real clinical distortion of the compressed signal. The method is based on the wavelet transform and its subsequent coding using the set partitioning in hierarchical trees (SPIHT) algorithm. By thresholding the WWPRD in the wavelet transform domain, a very precise reconstruction error can be achieved thus enabling to obtain clinically useful reconstructed signals. Because of its computational efficiency, the method is suitable to work in a real-time operation, thus being very useful for real-time telecardiology systems. The method is extensively tested using two different ECG databases. Results led to an excellent conclusion: the method controls the quality in a very accurate way not only in mean value but also with a low-standard deviation. The effects of ECG baseline wandering as well as noise in compression are also discussed. Baseline wandering provokes negative effects when using WWPRD index to guarantee quality because this index is normalized by the signal energy. Therefore, it is better to remove it before compression. On the other hand, noise causes an increase in signal energy provoking an artificial increase of the coded signal bit rate. Clinical validation by cardiologists showed that a WWPRD value of 10 preserves the signal quality and thus they recommend this value to be used in the compression system.

[1]  H A Fozzard,et al.  AZTEC, a preprocessing program for real-time ECG rhythm analysis. , 1968, IEEE transactions on bio-medical engineering.

[2]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[3]  Chih-Lung Lin,et al.  A quality-on-demand algorithm for wavelet-based compression of electrocardiogram signals , 2002, IEEE Transactions on Biomedical Engineering.

[4]  M.L. Hilton,et al.  Wavelet and wavelet packet compression of electrocardiograms , 1997, IEEE Transactions on Biomedical Engineering.

[5]  William A. Pearlman,et al.  Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm , 2000, IEEE Transactions on Biomedical Engineering.

[6]  N. Ahmed,et al.  Electrocardiographic Data Compression Via Orthogonal Transforms , 1975, IEEE Transactions on Biomedical Engineering.

[7]  Willis J. Tompkins,et al.  A New Data-Reduction Algorithm for Real-Time ECG Analysis , 1982, IEEE Transactions on Biomedical Engineering.

[8]  Roger G. Mark,et al.  The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it , 1990, [1990] Proceedings Computers in Cardiology.

[9]  Roger G. Mark,et al.  Evaluation of the 'TRIM' ECG data compressor , 1988, Proceedings. Computers in Cardiology 1988.

[10]  W. A. Coberly,et al.  ECG data compression techniques-a unified approach , 1990, IEEE Transactions on Biomedical Engineering.

[11]  Álvaro Alesanco Iglesias,et al.  Enhanced real-time ECG coder for packetized telecardiology applications , 2006, IEEE Transactions on Information Technology in Biomedicine.

[12]  Amara Lynn Graps,et al.  An introduction to wavelets , 1995 .

[13]  Amjed S. Al-Fahoum,et al.  Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure , 2006, IEEE Transactions on Information Technology in Biomedicine.

[14]  P. Laguna,et al.  ECG data compression with the Karhunen-Loeve transform , 1996, Computers in Cardiology 1996.