Asic Chip Design for Heart Rate Monitoring and Signal Processing

Authors

  • Sathesh Sathasivam Department of Elecrical and Computer Engineering, Adigrat University, Adigrat, India
  • Daniel Kiros Department of Elecrical and Computer Engineering, Adigrat University, Adigrat, India
  • J. Prakash Department of Information and Communication Engineering, Anna University, Chennai, India

DOI:

https://doi.org/10.53555/eee.v2i4.396

Keywords:

Application Specific Integrated Circuit,, Heart rate variability (HRV),, Adaptive filter,, Electrocardiogram (ECG)

Abstract

The objective of heart rate monitoring and processing devices is to perform automatic detection of cardiac arrhythmias in ECG signal. This work focuses on developing a sophisticated, small and reliable ASIC chip that can be used for monitoring and detecting the rate of heart beat for heart transplantation patient. Noise removal in heart rate signal is carried out by well known adaptive noise cancellation techniques such as LMS and RLS algorithms. In this work, ASIC chip is designed for heart rate monitoring and signal processing is done using LMS based adaptive algorithm. The proposed architectures have been modeled and verified for their functionality. Using the entire ASIC flow, suitable results obtained at various stages are compared and reported. The high computational requirement of all adaptive filtering algorithms has limited the scope of its use in medical applications. However, with rapid advances in VLSI technology, it is possible to implement complex circuits in a single chip. This work focuses on developing architectures for adaptive noise cancellation and its ASIC implementation.

Downloads

Download data is not yet available.

References

L. S ̈ornmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications. New York: Academic, 2005.

H.A.Kestler,M.Haschka,W.Kratz, F. Schwenker, G.Palm,V.Hombach, andM. Hoher, “De-noising of high-resolution ECG signals by combining the discrete wavelet transform with the Wiener filter,” Comput. Cardiol.,vol. 25, pp. 233–236, Sep. 1998.

V. X. Afonso, W. J. Tompkins, T. Q. Nguyen, K. Michler, and S. Luo, “Comparing stress ECG enhancement algorithms,” IEEE Eng. Med. Biol. Mag., vol. 15, no. 3, pp. 37–44, May 1996.

S. Poornachandra, “Wavelet-based denoising using subband dependent threshold for ECG signals,” Digital Signal Process., vol. 18, no. 1, pp. 49–55, Jan. 2008.

E.-S. A. El-Dahshan, “Genetic algorithm and wavelet hybrid scheme for ECG signal denoising,” Telecommun. Syst., vol. 46, no. 3, pp. 209–215, Mar. 2011.

S. Li and J. Lin, “The optimal de-noising algorithm for ECG using stationary wavelet transform,” in Proc. WRI World Congr. Comput. Sci. Inf. Eng., Mar. 2009, no. 6, pp. 469–473.

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved waveletdenoising via empirical Wiener filtering,” Proc. SPIE—Int. Soc. Opt. Eng., vol. 3169, pp. 389–399, Jul. 1997.

N.Nikolaev, Z. Nikolov, A. Gotchev, and K.Egiazarian, “Wavelet domain Wiener filtering for ECG denoising using improved signal estimate,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process, Jun. 2000, vol. 6, pp.3578–3581.

L. Chmelka and J. Kozumplik, “Wavelet-based Wiener filter for electrocardiogram signal denoising,” Comput. Cardiol., vol. 32, pp. 771–774, Sep. 2005.

L. Biel, O. Pettersson, L. Philipson, and P. Wide, “ECG Analysis: A new approach in human identification,” IEEE Trans. Instrum. Meas., vol. 50, no. 3, pp. 808–812, Jun. 2001.

E. Kaniusas, H. Pfützner, L. Mehnen, J. Kosel, J. C. Téllez-Blanco, G. Varoneckas, A. Alonderis, T. Meydan, M. Vázquez, M. Rohn, A. M. Merlo, and B. Marquardt, “Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG,” IEEE Sensors J., vol. 6, no. 3, pp. 819–828, Jun. 2006.

P. Laguna, R. Jané, S. Olmos, N. V. Thakor, H. Rix, and P. Caminal, “Adaptive estimation of QRS complex by the Hermite model for classification and ectopic beat detection,” Med. Biol. Eng. Comput., vol. 34, no. 1, pp. 58–68, Jan. 1996.

G. Ranganathan, R. Rangarajan, V. Bindhu, Estimation of heart rate signals formental stress assessment using neuro fuzzy technique, Applied Soft Computing, 12 (8) (2012) 1978-1984.

K. K. Parhi and T. Nishitani, “ VLSI architectures for discrete wavelet transforms,” IEEE Trans. Very Large Scale Integration Syst. , vol. 1, pp.191–202, June 1993.

Downloads

Published

2016-04-30

How to Cite

Sathasivam, S., Kiros, D., & Prakash, J. (2016). Asic Chip Design for Heart Rate Monitoring and Signal Processing. International Journal For Research In Electronics & Electrical Engineering, 2(4), 09–13. https://doi.org/10.53555/eee.v2i4.396