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Overview a Quality-Scalable and Energy-Efficient Approach for Spectral Analysis of Heart Rate Variability

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Authors:
Aditi Malviya, Neelesh Gupta, Neetu Sharma
10.5120/ijca2015905739

Aditi Malviya, Neelesh Gupta and Neetu Sharma. Article: Overview a Quality-Scalable and Energy-Efficient Approach for Spectral Analysis of Heart Rate Variability. International Journal of Computer Applications 129(7):7-10, November 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Aditi Malviya and Neelesh Gupta and Neetu Sharma},
	title = {Article: Overview a Quality-Scalable and Energy-Efficient Approach for Spectral Analysis of Heart Rate Variability},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {129},
	number = {7},
	pages = {7-10},
	month = {November},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

In this paper presenting bioelectrical signals which are spectrally analyzed for enabling energy- quality trade-offs, they are helpful in observing different health problems as those related with the rate of heart. To facilitate such trade-offs, the signals which are processed earlier are expressed primarily in a beginning in which considerable components that hold mainly of the related information can be simply notable from the components that effect the output to a smaller amount. Such an arrangement permits the pruning of operations allied with the less important signal components primary to power savings with loss of minor quality as simply less useful parts are reduced under the certain requirements. This provides the patients normal and abnormalities using ECG waves.

References

  1. World Health Organization., “Cardiovascular diseases,” 2009. [Online].
  2. M-k Suh, et al. “A Remote Patient Monitoring System for Congestive Heart Failure,” JMS, 2011.
  3. H. Mamaghanian, et al. “Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes,” IEEE TBE, 2011.
  4. Shimmer Research. (2008). [Online].
  5. R. F. Yazicioglu, et al., “Ultra-low-power wearable biopotential sensor nodes,” IEEE EMBS, 2009
  6. R. F. Yazicioglu and et al., “Ultra-low-power wearable biopotential sensor nodes,” in Proceedings of IEEE EMBS (EMBC’09), Sep. 2009.
  7. S. M. S. Jalaleddine, C. G. Hutchens, R. D. Stranttan, and W. A. Coberly, “ECG data compression techniques: A unified approach,” IEEE Trans. on Biomed. Eng., vol. 37, no. 4, pp. 329–343, 1990.
  8. L. S¨ornmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications. Elsevier Academic Press, 2005.
  9. M. B. Velasco, “Compression of electrocardiograms using cosine modulated filter banks and multi-resolution analysis (in spanish),” Ph.D. dissertation, University of Alcal´a, Spain, Dec. 2004.
  10. M. Hilton, “Wavelet and wavelet packets compression of electrocardiogram,” IEEE Trans. on Biomed. Eng., vol. 44, no. 5, pp. 394–402, 1997.
  11. Z. Lu, D. Y. Kim, and W. A. Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm,” IEEE Trans. On Biomed. Eng., vol. 47, no. 7, pp. 849–856, 2000.
  12. B. A. Rajoub, “An efficient coding algorithm for the compression of ECG signals using the wavelet transform,” IEEE Trans. on Biomed. Eng., vol. 49, no. 4, pp. 355–362, 2002.
  13. R. Benzid and et al., “Fixed percentage of wavelet coefficients to be zeroed for ECG compression,” Electronics Letters, vol. 39, no. 11, pp. 830–831, 2003.
  14. S. Aviyente, “Compressed sensing framework for EEG compression,” in Proceedings of the IEEE Workshop on Stat. Signal Proc. (SSP’07), Aug. 2007, pp. 181–184.
  15. S. S¸enay, L. F. Chaparro, M. Sun, and R. J. Sclabassi, “Compressive sensing and random filtering of EEG signals using slepian basis,” in Proceedings of the EURASIP EUSIPCO’08, Aug. 2008.
  16. F. Rincon, et. al, “Development and Evaluation of Multilead Wavelet-Based ECG Delineation Algorithms for Embedded Wireless Sensor Nodes,” IEEE TITB, 2011.
  17. F. Massien, et al. “Miniaturized wireless ECG monitor for real-time detection of epileptic seizures,” ACM TECS, 2013.
  18. AI Maistrou, “Implicit Comparison of Accuracy of Heart Rate Variability Spectral Measures Estimated via Heart Rate and Heart Period Signals,” IEEE CinC, 2008.
  19. J. F. Thayer, et al. “A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health,” NBR, 2012.
  20. W. H. Press, et al, “Fast algorithm for spectral analysis of unevenly sampled data,” Astrophysical Journal, 1989.
  21. S-Y Tseng, et al, “An effective heart rate variability processor design based on time-frequency analysis algorithm using windowed Lomb periodogram,” IEEE BioCAS, 2010.
  22. K. Kanoun, “A real-time compressed sensing-based personal electrocardiogram monitoring system,” IEEE DATE, 2011.

Keywords

Spectral Analysis, Heart Rate, Energy-Efficient, Haar, PSA