![](bg8.png)
cial data to the lab or clinic.
An additional improvement would be the appli-
cation of more sophisticated statistical analysis algo-
rithms, employed on the data in the learning period,
to determine ‘better’ parameters as input to the test
period of the program.
Another use of the model is for prerecorded ECG
data. Holter monitors are often used to gather ECG
data for an extended period of time, for later analy-
sis. Using the combinatorial model, this data could
be indexed using for example a suffix tree or suffix
array (Crochemore et al., 2007; Gusfield, 1997). For
an ECG of length n, this indexing can be done in O(n)
time.
Finally, further areas of investigation is to extend
the modeling of the ECG data to include more than
one lead, as well as to implement the beat classifica-
tion algorithm of Section 3.1.
REFERENCES
Afonso, V. X., Wieben, O., Tompkins, W. J., Nguyen, T. Q.,
and Luo, S. (1997). Filter bank-based ecg beat clas-
sification. In Proceedings of the 19th Annual Interna-
tional Conference of the IEEE, volume 1, pages 97–
100. Engineering in Medicine and Biology Society.
Aho, A. V. and Corasick, M. J. (1975). Efficient string
matching: an aid to bibliographic search. Commun.
ACM, 18(6):333–340.
ANSI/AAMI (1998a). EC38: Ambulatory Electrocardio-
graphs. Association for the Advancement of Medical
Instrumentation.
ANSI/AAMI (1998b). EC57: Testing and Reporting Per-
formance Results of Cardiac Rhythm and ST Segment.
Association for the Advancement of Medical Instru-
mentation.
Chazal, P. D., O’Dwyer, M., and Reilly, R. B. (2004). Auto-
matic classification of heartbeats using ecg morphol-
ogy and heartbeat interval features. IEEE Transac-
tions on Biomedical Engineering, 51:1196–1206.
Conover, M. B. (2002). Understanding Electrocardiogra-
phy. Mosby.
Crochemore, M., Hancart, C., and Lecroq, T. (2007). Algo-
rithms on Strings. Cambridge University Press.
Garratt, C. (2001). Mechanisms and Management of Car-
diac Arrhythmias. BMJ Books, London.
Goldberger, A. L., Amaral, L. A. N., Glass, L., Haus-
dorff, J. M., Ivanov, P. C., Mark, R. G., Mietus,
J. E., Peng, G. B. M. C. K., and Stanley, H. E.
(2000). PhysioBank, PhysioToolkit, and Phys-
ioNet: Components of a new research resource
for complex physiologic signals. Circulation,
101(23):e215–e220. Circulation Electronic Pages:
http://circ.ahajournals.org/cgi/content/full/101/23/
e215.
Gusfield, D. (1997). Algorithms on Strings, Trees and Se-
quences: Computer Science and Computational Biol-
ogy. Cambridge University Press.
Hu, Y. H., Palreddy, S., and Tompkins, W. J. (1997). A
patient-adaptable ecg beat classifier using a mixture
of experts approach. Biomedical Engineering, IEEE
Transactions on, 44(9):891–900.
Iliopoulos, C. S. and Michalakopoulos, S. (2009). A combi-
natorial model for ecg interpretation. In Proceedings
of ICMIBE 2009, International Conference on Med-
ical Informatics and Biomedical Engineering. World
Academy of Science.
Kawachi, I., Sparrow, D., Vokonas, P., and Weiss, S. (1995).
Decreased heart rate variability in men with phobic
anxiety (data from the normative aging study). The
American journal of cardiology, 75(14):882–885.
Massachusets Institute of Technology (1999). Mit-bih ecg
database. Available: http://ecg.mit.edu/.
Portet, F. (2008). P wave detector with pp rhythm tracking:
evaluation in different arrhythmia contexts. Physio-
logical Measurement, 29(1):141–155.
Springhouse (Editor) (2007). ECG Interpretation Made In-
credibly Easy! Lippincott Williams & Wilkins, 4
th
edition.
Stein, E. (2000). Rapid Analysis of Arrhythmias. Lippincott
Williams & Wilkins, 3
rd
edition.
Task Force of the European Society of Cardiology the
North American Society of Pacing Electrophysiology
(1996). Heart Rate Variability : Standards of Measure-
ment, Physiological Interpretation, and Clinical Use.
Circulation, 93(5):1043–1065.
Thaler, M. S. (2006). The Only EKG Book You’ll Ever Need.
Lippincott Williams & Wilkins, 5
th
edition.
COMBINATORIAL DETECTION OF ARRHYTHMIA
187