Tesis
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson
Date
2017-07-26Registration in:
Author
Pereira, Clayton Reginaldo
Institutions
Abstract
Currently, it is not a trivial task to point out a test that can diagnose accurately enough a patient
with Parkinson’s Disease, as well as it is quit difficult to assess the level of the disease.
Experts recommend the application of different types of tests, many of them based on signs
and biomedical imaging, such as electroencephalogram, computed tomography and magnetic
resonance to aid the detection of the disease process, since as the age ranges, symptoms
such as fatigue and weakness can hide diagnosis. In order to provide a more effective clinical
information to doctors aiming at diagnosis with greater confidence, methodologies to
perform the fusion of different imaging modalities have become increasingly popular and
promising. Recently, the use of forms containing some activities using a biometric pen with
multi-sensors have been applied for the detection of Parkinson’s Disease by means of handwriting
analysis. However, information derived from the scanned image of the form itself,
and the one obtained by same pen have not been used together for this purpose. Thus, this
proposal aims using pattern recognition techniques and image processing aimed at using the
information from the form together with data from the pen. We believe a possible improvement
in the medical diagnosis of Parkinson’s Disease can be archived. Another contribution
of this proposal, is the design of a multimodal database to aid in the diagnosis of Parkinson’s
Disease.