![](bg6.png)
3.3.2 Classification
Now our output is defined by two classes: normal and
ill fish. The parameters used that leaded to a higher
accuracy for the SVM were the zero crossing rate, the
standard deviation, the maximum power using the pe-
riodogram, the maximum number of occurrences us-
ing the histogram, and the previous algorithm output.
The learning options used were for the kernel func-
tion the Sigmoid function (tanh(8 ∗ x.y)), a Cost of
2.0 (Model Complexity - penalty parameter) and a
numeric precision of 0.001. The accuracy obtained
using leave one out for the SVM method was 100%,
meaning that all cases analysed were correctly clas-
sified. On the other hand, the Naive Bayes method
based on the relative frequency presented a maximum
accuracy of 67.59% using the parameters standard de-
viation, the maximum power using the periodogram
and the previous algorithm output.
As we want to choose the classifier that predicts
the classes with a higher accuracy value we choose
the method SVM to build our final classifier. Because
the Orange program is open source, with the access to
the functions that build the classifier SVM we can use
them to construct the final algorithm in python.
3.3.3 Final Algorithm
Now it’s possible to built the final algorithm. First we
prepare the data with the removal of the initial peak
from the main device, the application of a filter to ex-
clude possible noise, the normalization of the data and
the smooth of the signal using a Hanning window of
0.05 seconds. Then we use the classifier to predict if
the fish is normal or ill. Consequently, according to
the classification it’s possible to characterize the be-
haviour in number of tail-flips per minute using the
corresponding hypothesis that consists in the use of
the parameter zero crossing rate. The final result will
present the classification, the probability for that clas-
sification, and the number of tail-flips per minute.
4 CONCLUSIONS
A new algorithm was developed to classify and char-
acterize the behaviour of zebrafish. To facilitate its
use, the algorithm should be integrated in the plat-
form Open Signals. The fact that this algorithm uses
classification can be an advantage as it may bring an
efficient separation between a healthy fish from one
that has been genetically modified to have PD. Also,
the algorithm should be applied in a case study as ex-
ecuted by (Correia, Ana Dulce and Soares, Rui S. and
Sousa, Sara and Outeiro, Tiago F. and Afonso, Nuno
and Willemsen, Rob and Herma van der Linde, 2012),
to verify that the responses are in agreement with the
fish behaviour and literature. This algorithm may be
useful for further studies not only related with PD, but
any other that uses zebrafish behaviour as an end point
to study human diseases.
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