masterThesis
Utilização de técnicas de aprendizado de máquina para predição de crises epiléticas
Fecha
2016-07-28Registro en:
SANTOS, Kelyson Nunes dos. Utilização de técnicas de aprendizado de máquina para predição de crises epiléticas. 2016. 73f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2016.
Autor
Santos, Kelyson Nunes dos
Resumen
Event prediction from neurophysiological data has many variables which must be analyzed
in di erent moments, since data acquisition and registry to its post-processing. Hence,
choosing the algorithm that will process these data is a very important step, for processing
time and accuracy of results are determinant factors for a diagnosis auxiliary tool. Tasks of
classi cation and prediction also help in understanding brain cell's networks interactions.
This work uses Supervised Machine Learning techniques with different features to analyze
their impact on the task of epileptic seizure prediction from canine neurophysiological data
and purposes using of ensembles to optimize the performance of event prediction task
through computational low-cost techniques. Epileptic dogs' EEG data were preprocessed
throug Fourier transform and only significant frequencies were considered (1 to 30Hz).
It was applied a dimensionality reductor and then data was submitted to supervised
machine learning techniques. Two scenarios were evaluated: first used raw data resulted
from Fourier transform, as the second one transform these data. Algorithms evaluation
was made through area under ROC curve (AUC) measure. Best results were to scenario A
(a) an heterogeneous ensemble formed by a KNN, a decision tree and a bayesian classifier,
scoring 0.7074 and (b) an example of decision tree evaluated in 0.687, and, for scenario B,
best results were (a) a setup of decision tree which obtained 0.620 and (b) an heterogeneous
ensemble composed by a KNN, a decision tree and a bayesian classifier, scoring 0.612.