![](bg6.png)
the decision hyperplane.
In our tests we used 2 seizures in the test set using
10 randomizations. Therefore, the resolution of the
sensitivity is only 5%. This is also visible in the tables
showing the results.
For using the SMOTE technique, we have only a
limited number of seizures in our setup when using
the 3-fold cross-validation in determining the optimal
parameters for the SVM. The new data points are gen-
erated on the line segments connecting the minority
class examples, but sometimes there is only one near-
est neighbor (for determining the line segments) for
generating new data points. This can explain why
the SMOTE technique gives lower results, although
it also works well for patient A and C.
We also evaluated a cost function taking into ac-
count the decision values of the SVM classification
(indicating the distance from the data points to the
decision plane). However, this did not give any better
results compared to our original cost function, in most
cases the performance was even lower.
5 CONCLUSIONS
We have tested different approaches to overcome
the imbalance problem in our application of detect-
ing nocturnal epileptic seizures in children using ac-
celerometers. Oversampling of the minority class
seems to give the best results, especially the density
estimation oversampling. On 2 of 3 patients, this tech-
nique gives a sensitivity of 95% or more and a PPV
more than 70%.
ACKNOWLEDGEMENTS
Research supported by Research Council KUL:
GOA-MANET, IWT: TBM070713-Accelero, Bel-
gian Federal Science Policy Office IUAP P6/04
(DYSCO, ’Dynamical systems, control and optimiza-
tion, 2007-2011); EU: Neuromath (COSTBM0601).
Kris Cuppens is funded by a Ph.D. grant of the
Agency for Innovation by Science and Technology
(IWT).
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