Auditory Brainstem Response (ABR) results from the combination of neural activity responses in the presence of sound stimuli, detected by the cortex and characterized by peaks and valleys. They are identified by Roman numerals (I, II, III, IV, V, VI and VII). The identification of these peaks is carried out by the classical manual process of analysis, which is based on the visual/manual processing of the signals. The morphological and temporal characteristics of the signal carry relevant physiological and anatomical information regarding the auditory system. However, in this visual process of analysis some difficulties may occur, specifically, the results of the analysis may vary according to the type of protocol, settings of equipment employed, and the experience of the examiner. This makes the analysis of ABR subject to the influence of many variables that may interfere on the reliability and agreement of results obtained in distinct research centers and by different examiners. Therefore, the main propose of this study was to develop and assess a system capable of automatically detecting and classifying ABR waves, which are called Jewett waves. A relevant feature of the proposed tool is that it can learn from the experience of examiners continuously. In order to evaluate the system approximately 748 samples of ABR obtained from 11 subjects were analyzed by the automatic system. These results were compared to analyses obtained from five seasoned examiners, and they showed a high level of agreement, ranging for 74.3% to 99.7%, between responses given by the system and the examiners. Thus the proposed technique is proved to be accurate even in the presence of noise, especially characteristic of the ABR that is a sign of low amplitude.
Auditory brainstem response; Automatic detection; Continuous Wavelet Transform; Linear regression
