Segmentation of auditory brainstem response signals

Abstract

Auditory brainstem responses are used to detect hearing defects in audiology and otoncurology. The use of computer programs for the analysis of such recordings is increasing. To identify their detailed properties a pattern recognition algorithm implemented in an analysis program must be highly reliable. For the recognition process, some preprocessing phases after recording are necessary, such as filtering and often also segmentation. In the following, we will explore segmentation, which can be used in preprocessing of biomedical signals after filtering. We studied linear segmentation, where slopes of short signal segments are computed and divided into different classes according to their values. A segment length of 8 samples for a sampling frequency of 50 kHz employed was best according to our tests and error criteria. Using clustering, we found that less than 10 segment classes is suitable for pattern recognition.