Automatic Real-Time ECG Coding Methodology Guaranteeing Signal Interpretation Quality

This paper introduces a new methodology for compressing ECG signals in an automatic way guaranteeing signal interpretation quality. The approach is based on noise estimation in the ECG signal that is used as a compression threshold in the coding stage. The Set Partitioning in Hierarchical Trees algorithm is used to code the signal in the wavelet domain. Forty different ECG records from two different ECG databases commonly used in ECG compression have been considered to validate the approach. Three cardiologists have participated in the clinical trial using mean opinion score tests in order to rate the signals quality. Results showed that the approach not only achieves very good ECG reconstruction quality but also enhances the visual quality of the ECG signal.

[1]  José García,et al.  ECG signal compression plus noise filtering with truncated orthogonal expansions , 1999, Signal Process..

[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]  Álvaro Alesanco Iglesias,et al.  A Simple Method for Guaranteeing ECG Quality in Real-Time Wavelet Lossy Coding , 2007, EURASIP J. Adv. Signal Process..

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

[5]  A. Cohen,et al.  ECG compression using long-term prediction , 1993, IEEE Transactions on Biomedical Engineering.

[6]  I. S. N. Murthy,et al.  ECG Data Compression Using Fourier Descriptors , 1986, IEEE Transactions on Biomedical Engineering.

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

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

[9]  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.

[10]  P. Laguna,et al.  Adaptive estimation of QRS complex wave features of ECG signal by the hermite model , 2007, Medical and Biological Engineering and Computing.

[11]  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.

[12]  K. R. Rao,et al.  Orthogonal Transforms for Digital Signal Processing , 1979, IEEE Transactions on Systems, Man and Cybernetics.

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

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

[15]  B. Bradie,et al.  Wavelet packet-based compression of single lead ECG , 1996, IEEE Transactions on Biomedical Engineering.

[16]  G.D. Barlas,et al.  A novel family of compression algorithms for ECG and other semiperiodical, one-dimensional, biomedical signals , 1996, IEEE Transactions on Biomedical Engineering.

[17]  Arnon D. Cohen,et al.  The weighted diagnostic distortion (WDD) measure for ECG signal compression , 2000, IEEE Transactions on Biomedical Engineering.

[18]  W.J. Tompkins,et al.  Compression of the ambulatory ECG by average beat subtraction and residual differencing , 1991, IEEE Transactions on Biomedical Engineering.