Electrocardiogram data compression using DCT based discrete orthogonal Stockwell transform

Abstract This paper reports a novel electrocardiogram (ECG) data compression algorithm which employs DCT based discrete orthogonal Stockwell transform. Dead-zone quantization is utilized to apply quantization as well as a threshold condition to transform coefficients. Further, integer conversion of coefficients is performed. It improves compression at the cost of very less reconstruction error. All integer coefficients are encoded using run-length coding. It exploits the repetition of data instances. Run-length coding helps to achieve higher compression without any relevant information loss. Performance of the proposed compression algorithm is evaluated using 48 single channel ECG records which are taken from the MIT-BIH arrhythmia database. A competitive compression performance is observed in comparison with other ECG compression methods.

[1]  Chandan Kumar Jha,et al.  ECG data compression algorithm for tele-monitoring of cardiac patients , 2017 .

[2]  Xinling Shi,et al.  ECG compression using uniform scalar dead-zone quantization and conditional entropy coding. , 2008, Medical engineering & physics.

[3]  Chandan Kumar Jha,et al.  Classification and Compression of ECG Signal for Holter Device , 2018 .

[4]  Giuliano Grossi,et al.  High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis , 2015, Digit. Signal Process..

[5]  K.M. Buckley,et al.  ECG data compression using cut and align beats approach and 2-D transforms , 1999, IEEE Transactions on Biomedical Engineering.

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

[7]  Yunhua Zhang,et al.  ECG compression based on wavelet transform and Golomb coding , 2006 .

[8]  Vinod Kumar,et al.  Improved modified AZTEC technique for ECG data compression: Effect of length of parabolic filter on reconstructed signal , 2005, Comput. Electr. Eng..

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

[10]  W. S. Kuklinski,et al.  Fast Walsh transform data-compression algorithm: E.c.g. applications , 1983, Medical and Biological Engineering and Computing.

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

[12]  S. K. Mukhopadhyay,et al.  An ECG signal compression technique using ASCII character encoding , 2012 .

[13]  Nai-Kuan Chou,et al.  ECG data compression using truncated singular value decomposition , 2001, IEEE Trans. Inf. Technol. Biomed..

[14]  Anil Kumar,et al.  Beta wavelet based ECG signal compression using lossless encoding with modified thresholding , 2013, Comput. Electr. Eng..

[15]  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.

[16]  Myoungho Lee,et al.  A Real-Time ECG Data Compression and Transmission Algorithm for an e-Health Device , 2011, IEEE Transactions on Biomedical Engineering.

[17]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[18]  Edward R. Vrscay,et al.  The Discrete Orthonormal Stockwell Transform and Variations, with Applications to Image Compression , 2013, ICIAR.

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

[20]  Xingyuan Wang,et al.  A 2-D ECG compression algorithm based on wavelet transform and vector quantization , 2008, Digit. Signal Process..

[21]  A. Cohen,et al.  Compression of ECG Signals Using Vector Quantization , 1990, IEEE South African Symposium on Communications and Signal Processing.

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

[23]  Xiaoyan Xiang,et al.  Electrocardiograph compression based on sifting process of empirical mode decomposition , 2016 .

[24]  Robert Glenn Stockwell,et al.  A basis for efficient representation of the S-transform , 2007, Digit. Signal Process..

[25]  Chia-Chun Sun,et al.  A 2-D ECG compression method based on wavelet transform and modified SPIHT , 2005, IEEE Trans. Biomed. Eng..

[26]  Chandan Kumar Jha,et al.  A novel ECG data compression algorithm using best mother wavelet selection , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[27]  Abdel-Ouahab Boudraa,et al.  On signals compression by EMD , 2012 .

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

[29]  Urs E. Ruttimann,et al.  Compression of the ECG by Prediction or Interpolation and Entropy Encoding , 1979, IEEE Transactions on Biomedical Engineering.

[30]  Nowshad Amin,et al.  ECG Compression Using Subband Thresholding of the Wavelet Coefficients , 2011 .

[31]  Arnon D. Cohen,et al.  ECG signal compression using analysis by synthesis coding , 2000, IEEE Transactions on Biomedical Engineering.

[32]  Bashar A. Rajoub An efficient coding algorithm for the compression of ECG signals using the wavelet transform , 2002, IEEE Transactions on Biomedical Engineering.

[33]  A. Djohan ECG COMPFtESSION USING DISCRETE TRANSFORM SYMMETRIC WAVELET , 1995 .

[34]  Chandan Kumar Jha,et al.  Efficient ECG data compression and transmission algorithm for telemedicine , 2016, 2016 8th International Conference on Communication Systems and Networks (COMSNETS).