Sparse Encoding Algorithm for Real-Time ECG Compression

  • Rohan Basu Roy
  • Arani Roy
  • Amitava Mukherjee
  • Alekhya Ghosh
  • Soham Bhattacharyya
  • Mrinal K. Naskar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 727)

Abstract

In this paper, we propose a sparse encoding algorithm consisting of two schemes namely geometry-based method (GBM) and the wavelet transform-based iterative thresholding (WTIT). The sub-algorithm GBM reduces the minimal ECG voltage values to zero level. Subsequently, WTIT encodes the ECG signal in time-frequency domain, obtaining high sparsity levels. Compressed Row Huffman Coding (CRHC) algorithm converts the sparse matrices into compressed, transmittable matrices. The performance of the algorithms is validated in terms of compression ratio (CR), percentage RMS difference (PRD), and time complexity.

Keywords

Sparse matrix Real-time ECG compression Wavelet transform Iterative thresholding Transmittable matrix 

References

  1. 1.
    Polania L (2011) Compressed sensing based method for ECG compression. In:  Proceedings IEEE international conference acoustic, speech signal processes, pp 761–764Google Scholar
  2. 2.
    Mamaghanian H (2011) Compressed sensing for realtime energy-efficient ECG compression on wireless body sensor nodes. IEEE Trans Biomed Eng 58(9):2456–2466CrossRefGoogle Scholar
  3. 3.
    Mishra SK, Panda G, Meher S (2009) Chebyshev functional link artificial neural networks for denoising of image corrupted by salt and pepper noise. Int J Recent Trends Eng 1:413–417Google Scholar
  4. 4.
    Hilton ML (1997) Wavelet and wavelet packet compression of electrocardiograms. IEEE Trans Biomed Eng 44:394–402CrossRefGoogle Scholar
  5. 5.
    Dongarra J (2000) Sparse matrix storage formats. In: Templates for solution of algebraic eigenvalue problems: a practical guide, SIAMGoogle Scholar
  6. 6.
    Moody GB, Mark RG MIT-BIH database. http://www.physionet.org/physiobank/database/mitdb
  7. 7.
    Chae DH (2013) Performance study of compressive sampling for ECG signal compression in noisy and varying sparsity acquisition. In: IEEE international conference on acoustic, speech and signal process, pp 1306–1309Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rohan Basu Roy
    • 1
  • Arani Roy
    • 2
  • Amitava Mukherjee
    • 3
  • Alekhya Ghosh
    • 1
  • Soham Bhattacharyya
    • 1
  • Mrinal K. Naskar
    • 2
  1. 1.Institute of Radio Physics and ElectronicsUniversity of CalcuttaKolkataIndia
  2. 2.Department of ETCEJadavpur UniversityKolkataIndia
  3. 3.IBM India Private LimitedKolkataIndia

Personalised recommendations