Application of Digital Signal Processing Techniques to Biological Signals

Zahra Moussavi, University of Manitoba

Abstract

Advances in computer technology and application of digital signal processing on biological signals over the last few decades have opened a new era in medicine not only from its diagnostic application perspective but also from the abstract scientific perspective in understanding and modeling the biological mechanism in producing the signal. Many researchers have applied linear and nonlinear analysis techniques for modeling, diagnosis, estimation and providing an alternative method for some of the invasive medical assessments. While the linear analysis is desirable due to its simplicity, however usually biological systems are too complicated to lend themselves to linear models. Hence, in recent years, there has been a trend in shifting the research toward nonlinear analysis of the biological signals.

The role of biomedical engineering in general and application of signal processing techniques in particular, in improving health care system has been significant more than ever in the last two decades. According to the U.S. Department of Labor's report there will be 31.4% increase in biomedical engineering jobs in the next five years. The report attributed the increase to aging U.S. population and the increasing demand for improved medical devices and systems. The current trends in health care delivery systems, which encompass medical device enterprise, embrace a reduction of hospital labor costs, increase in outpatient surgical procedures and progressive home health care including self-diagnosis and self-therapy. In addition, automated and innovative diagnosis by signal and image processing techniques define new developments and research activities in medicine and engineering.

In this presentation, a few of the major applications of linear and nonlinear signal processing techniques to biological signals are introduced and discussed. In particular, advances in the following topics will be addressed:
- Respiratory sounds analysis;
- Swallowing sound modeling and diagnosis of swallowing disorders;
- Dynamics of human postural control and balance; and
- Human motor learning.

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