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Multistage compression of quasiperiodic signals


S.P. Panko, A.V. Mishurov, V.V. Evstratko, A.A. Gorchakovskii

The article compares the most common compression methods of quasiperiodic signals. Multistage compression algorithm of ECG signals is described. Compression of digitized quasiperiodic signals, which also include electrocardiographic (ECG) signals, is a very important line of telemedicine services development. It is especially useful in remote Holter monitoring, associated with transmission a lot of information via telecommunications networks. ECG signals quasiperiodicity explained by the influence of respiratory and otherhuman activity, rhythm disturbances, which are called «Arrhythmia» and other individual characteristics. Improvement of ECG compression algorithms are motivated, above all, the desire to reduce traffic and decrease the associated costs, which should be met by the patient.

  1. Cox J.R. et al. AZTEC: A preprocessing program for real-time ECG rhythm analysis // IEEE Trans. Biomed. Eng. 1968. № 15. P. 128–129.
  2. Hamilton P.S., Tompkins W.J. Compression of the ambulatory ECG by average beat subtraction and residual differencing // IEEE Trans Biomed Eng. 1991. V. 38(3). P. 253–259.
  3. Sweeney R.J. Patent Application 20090192395 (US).
  4. Brito M., Henriques J., Carvalho P., Ribeiro B., Antunes M. An ECG compression approach base on a segment dictionary and Besier approximation // 15th European Signal Processing Conference (EUSIPCO 2007). Poznan, Poland. September 3–7 2007.
  5. Fira C.M., Goras L. An ECG signals compression method and its validation using neural networks // IEEE Transaction on biomedical engineering. 2008. V. 55. № 4.
  6. MIT-BIH arrhythmia database. physiobank/database/mitdb/
  7. Pooyan M., Taheri A., Moazami-Goudarzi M., Saboori I. Wavelet compression of ECG signals using SPIHT algorithm // International Journal of Information and Communication Engineering. 1:4. 2005.
  8. Bruce M. Patent № 5215098 (US).
  9. Sriraam N., Eswaran C. performance evaluation of lossless two-stage compression schemes for EEG signal // International Journal of Signal Processing (IJSP). 2004. V. 1. № 2.
  10. Iskandar R., I Wayan Simri W. Compression of ECG signal using neural network predictor and Huffman Coding. Universitas Gunadarma (Indonesia). Proceeding Seminar Ilmiah Nasional KOMMIT 24-11-2010.
  11. Batista L.V., Melcher E., Carvalho L.C. Compression of ECG signals by optimized quantization of discrete cosine transform coefficients // Medical Engineering and Physics. 2001. V. 23. Iss. 2. P. 127–134.
  12. Pinheiro E., Postolache O., Girão P. Evaluation of Compressed Sensing Impact in Cardiac Signals Processing and Transmission / 2012 SIAM Conference on Applied Linear Algebra Minisimposium Application of compressed sensing in bio-medicine. Valencia. Spain. June 18th. 2012.

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