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classification problem. The second most important
features are morphological features. The other feature
groups such as Hermite basis function expansion co-
efficients, higher-order autocorrelation statistics and
patient-normalized features do not seem to serve the
classification performances.
These results obtained with the weighted SVM
model and R-R intervals combined to segmentation
features are significantly better than previously re-
ported inter-patient classification models. In particu-
lar, the classification performances for the pathologi-
cal classes are always improved with more than 80%;
those classes are of crucial importance for the diag-
nosis. Furthermore, these performances are achieved
with a reduced number of features. The choice of the
features is thus a task of major importance, as a bad
selection or too many features can lead to unaceptable
results.
Another important issue for classification of heart
beats resides in the class unbalance, which is met with
weights being included in the SVM model. Indeed,
the average accuracy obtained by the model with our
best feature selection decreases from 83.0% to 54.3%,
with an accuracy of only 7.4% for class S and of
33.0% for class F when these weights are removed,
leading to rather useless models that are unable to
grasp the importance of the pathological cases.
ACKNOWLEDGEMENTS
G. de Lannoy is funded by a Belgian F.R.I.A. grant.
This work was partly supported by the Belgian
“R
´
egion Wallonne” ADVENS and DEEP projects.
REFERENCES
Association for the Advancement of Medical Instrumenta-
tion (1998). Testing and reporting performance results
of cardiac rhythm and st segment measurement algo-
rithms. ANSI/AAMI EC38:1998.
Chazal, P. D., O’Dwyer, M., and Reilly, R. B. (2004). Auto-
matic classification of heartbeats using ecg morphol-
ogy and heartbeat interval features. Biomedical Engi-
neering, IEEE Transactions on, 51:1196–1206.
Cheng, W. and Chan, K. (1998). Classification of electro-
cardiogram using hidden markov models. Engineer-
ing in Medicine and Biology Society, 1998. Proceed-
ings of the 20th Annual International Conference of
the IEEE, 1:143–146.
Christov, I., G
´
omez-Herrero, G., Krasteva, V., Jekova, I.,
Gotchev, A., and Egiazarian, K. (2006). Comparative
study of morphological and time-frequency ecg de-
scriptors for heartbeat classification. Med. Eng. Phys.,
28(9):876–887.
Clifford, G. D., Azuaje, F., and McSharry, P. (2006). Ad-
vanced Methods And Tools for ECG Data Analysis.
Artech House, Inc., Norwood, MA, USA.
Franc¸ois, D. (2008). Feature selection. In Wang, J., ed-
itor, Encyclopedia of data mining and warehousing,
second edition, Information Science Reference.
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov,
P. C., Mark, R., Mietus, J., Moody, G., Peng, C.-K.,
and Stanley, H. (2000). PhysioBank, PhysioToolkit,
and PhysioNet: Components of a new research re-
source for complex physiologic signals. Circulation,
101(23):e215–e220.
Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. A.
(2006). Feature Extraction: Foundations and Appli-
cations (Studies in Fuzziness and Soft Computing).
Springer-Verlag New York, Inc., Secaucus, NJ, USA.
Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt,
L., and Sornmo, L. (2000). Clustering ecg com-
plexes using hermite functions and self-organizing
maps. Biomedical Engineering, IEEE Transactions
on, 47(7):838–848.
Melgani, F. and Bazi, Y. (2008). Classification of electro-
cardiogram signals with support vector machines and
particle swarm optimization. Information Technology
in Biomedicine, IEEE Transactions on, 12(5):667–
677.
Osowski, S. and Hoai, L. (2001). Ecg beat recognition us-
ing fuzzy hybrid neural network. Biomedical Engi-
neering, IEEE Transactions on, 48(11):1265–1271.
Osowski, S., Hoai, L., and Markiewicz, T. (2004). Sup-
port vector machine-based expert system for reliable
heartbeat recognition. Biomedical Engineering, IEEE
Transactions on, 51(4):582–589.
Park, K., Cho, B., Lee, D., Song, S., Lee, J., Chee, Y., Kim,
I., and Kim, S. (2008). Hierarchical support vector
machine based heartbeat classification using higher
order statistics and hermite basis function. In Com-
puters in Cardiology, 2008, pages 229–232.
Platt, J. C. (1999). Fast training of support vector machines
using sequential minimal optimization. In Scholkopf,
B., Burges, C., and Smola, A., editors, Advances in
Kernel Methods. The MIT Press.
Vapnik, V. N. (1999). The Nature of Statistical Learning
Theory (Information Science and Statistics). Springer.
Yinggang, Z. and Qinming, H. (2006). An unbalanced
dataset classification approach based on v-support
vector machine. In Intelligent Control and Automa-
tion, 2006. WCICA 2006. The Sixth World Congress
on, volume 2, pages 10496–10501.
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