Decomposição de potenciais evocados auditivos do tronco encefálico por meio de classificador probabilístico adaptativo

Abstract

The Auditory Brainstem Respose signals are characteristic of the combination of neural activity responses in presence of sound stimuli, detected by the cortex and characterized by peaks and valleys. They are named by roman numerals (I, II, III, IV, V, VI and VII). The identification of these peaks is made by the classic manual process of analysis, which is based on the visualization of the signal generated by the sum of each sample. In the sum the morphological characteristics of the signal and the temporal aspects relevant waves made by Jewett are identified. However, in this visual process some difficulties may occur, regarding the recognition of patterns present, which may vary according to local, individual equipment and settings in the selected protocol. Making the analysis of ABR subject to the influence of many variables and a constant source of doubt about the reliability and agreement between examiners. In order to create a system to automatic detection of these peaks and self-learning, that takes into account the profile for evaluate from examiners this work was developed. The continuous wavelet transforms an innovative technique for the detection of peaks was used associate with a probabilistic model for classification based on the histograms with information provide by examiners. In evaluating of the system, based on the swat rate between the system and a manual technique an accuracy ranging for 74.3% to 99.7%, according to each waves. Thus the proposed technique is proved to be accurate especially in ABR that is a sign of low amplitude.