Authors:
Kris Cuppens
1
;
Peter Karsmakers
1
;
Anouk Van de Vel
2
;
Bert Bonroy
3
;
Milica Milosevic
4
;
Lieven Lagae
2
;
Berten Ceulemans
2
;
Sabine Van Huffel
4
and
Bart Vanrumste
1
Affiliations:
1
Thomas More Kempen and KU Leuven, Belgium
;
2
University Hospital Leuven, Belgium
;
3
Thomas More Kempen, Belgium
;
4
KU Leuven, Belgium
Keyword(s):
Epilepsy Detection, Acceleration Data, Unbalanced Data, Support Vector Machines.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Health Engineering and Technology Applications
;
Learning of Action Patterns
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
Data of nocturnal movements in epileptic patients is marked by an imbalance due to the relative small number of seizures compared to normal nocturnal movements. This makes developing a robust classifier more difficult, especially with respect to reducing the number of false positives while keeping a high sensitivity. In this paper we evaluated different ways to overcome this problem in our application, by using a different weighting of classes and by resampling the minority class. Furthermore, as we only have a limited number of training
samples available per patient, additionally it was investigated in which manner the training set size affects the results. We observed that oversampling gives a higher performance than only adjusting the weights of both classes. Compared to its alternatives oversampling based on the probability density function gives the best results. On 2 of 3 patients, this technique gives a sensitivity of 95% or more and a PPV more than 70%. Furthermore, an increa
sed imbalance in the dataset leads to lower performance, whereas the size of the dataset has little influence.
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