Luna-Guevara GR

Language: Spanish
References: 17
Page: 42-50
PDF size: 310.52 Kb.

ABSTRACT

Introduction: Depression is the most common disease, either separately or in comorbidity with other clinical entities. It is a disease that generally goes unnoticed for professionals and families, hurting the patient care outcomes, being her second leading cause of disability worldwide, by 2020.
Objective: To record electroencephalogram in patients with some type of depression, apply techniques to extract relevant information and by means of a discriminant analysis to generate a prediction and classification of symptoms.
Methods: We performed a statistical analysis on the registration of electroencephalogram with 19 channels, variable, for 23 patients. Extractions of features through an analysis of components for each of the subjects was conducted in all variables validating tests and subsequently through a linear classifier obtain a prognosis on the basis of the obtained statistical
Results: We collected statistics classification, Fisher linear discriminant functions, noting that there are significant differences between the two populations studied and that the selected variables impact on those differences, was built a discriminant function, for populations (healthy and with depression). 0.635 canonical correlation indicates that on average, every 100 subjects which are classified, between 67 and 68 of these are classified correctly, which is a rate acceptable and reliable.
Conclusions: These results allow to conclude that a classifier based on statistics can be reliable and that there is a panorama open as to improve accuracy, accuracy of diagnosis. Considering the spectral bands of interest domain.

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