Biomedical Engineering Reference
In-Depth Information
28.
Find the first four outputs of the discrete system
y
½
k
2
y
½
k
1
þ
2
y
½
k
2
¼
f
½
k
2
if
y
½
1
¼
1,
y
½
2
¼
0, and
f
½
k
¼
u
½
k
:
29.
In MATLAB, design a routine to show that averaging random noise across many trials
approaches zero as the number of trials increases.
30.
Accurate measurements of blood glucose levels are needed for the proper treatment of
diabetes. Glucose is a primary carbohydrate that circulates throughout the body and serves as
an energy source for cells. In normal individuals the hormone insulin regulates the levels of
glucose in the blood by promoting glucose transport out of the blood to skeletal muscle and
fat tissues. Diabetics suffer from improper management of glucose levels, and the levels of
glucose in the blood can become too high. Describe how fuzzy logic might be used in the
control of a system for measuring blood glucose levels. What advantages would the fuzzy
logic system have over a more conventional system?
31.
Describe three different biosignal processing applications for which artificial neural networks
might be used. Give at least two advantages of artificial neural networks over traditional
biosignal processing methods for the applications you listed.
32.
The fuzzy sets in Example Problem 11.28 have been calibrated so a person with a resting heart
rate of 95 beats per minute has a 75 percent degree of membership in the normal category and
a 25 percent degree of membership in the tachycardia category. A resting heart rate of 65
beats per minutes indicates a 95 percent degree of membership in the normal category. Draw
a graph of the fuzzy sets.
Suggested Readings
M. Akay, Biomedical Signal Processing, Academic Press, Inc., San Diego, CA, 1994.
M. Akay (Ed.), Time Frequency and Wavelets in Biomedical Signal Processing, IEEE Press, New York, NY, 1998.
P. Bauer, S. Nouak, R. Winkler, A Brief Course in Fuzzy Logic and Fuzzy Control, Fuzzy Logic Laboratorium Linz-
Hagenberg, Linz, Austria, 1996.http://www.flll.uni-linz.ac.at/fuzzy.
C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press Inc., New York, NY, 1995.
E.N. Bruce, Biomedical Signal Processing and Signal Modeling, Wiley-Interscience, New York, NY, 2000.
E.J. Ciaccio, S.M. Dunn, M. Akay, Biosignal Pattern Recognition and Interpretation Systems: Part 1 of 4: Fundamen-
tal Concepts, IEEE Eng. Med. Biol. Mag. 12 (1993) 810-897.
E.J. Ciaccio, S.M. Dunn, M. Akay, Biosignal Pattern Recognition and Interpretation Systems: Part 2 of 4: Methods
for Feature Extraction and Selection, IEEE Eng. Med. Biol. Mag. 12 (1993) 106-113.
E.J. Ciaccio, S.M. Dunn, M. Akay, Biosignal Pattern Recognition and Interpretation Systems: Part 3 of 4: Methods of
Classification, IEEE Eng. Med. Biol. Mag. 12 (1994) 269-279.
E.J. Ciaccio, S.M. Dunn, M. Akay, Biosignal Pattern Recognition and Interpretation Systems: Part 4 of 4: Review of
Applications, IEEE Eng. Med. Biol. Mag. 13 (1994) 269-273.
A. Cohen, Biomedical Signal Processing: Volume I Time and Frequency Domain Analysis, CRC Press, Boca Raton,
FL, 1986.
A. Cohen, Biomedical Signal Processing: Volume II Compression and Automatic Recognition, CRC Press, Boca
Raton, FL, 1986.
J. Dempster, Computer Analysis of Electrophysiological Signals, Academic Press Inc., San Diego, CA, 1993.
S.R. Devasahayam, Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems
Modeling, Kluwer Academic, New York, NY, 2000.
