Abstract
The auditory brainstem response (ABR) has become a routine clinical tool for hearing and neurological assessment. In order to pick out the ABR from the background EEG activity that obscures it, stimulus-synchronized averaging of many repeated trials is necessary and typically requires up to 2000 repetitions. This amount of repetitions could be very difficult and uncomfortable for some subjects. In this study a method based on wavelet analysis is introduced to reduce the required number of repetitions. The important features of the ABR are extracted by thresholding and matching the wavelet coefficients. The rules for the detection of the ABR peaks are obtained from the training data and the classification is carried out after a suitable threshold is chosen. This approach is also validated by another three sets of test data. Moreover, two procedures based on Woody averaging and latency correlated averaging are used to preprocess the ABR, which enhance the classification results.
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Zhang, R., McAllister, G., Scotney, B., McClean, S., Houston, G. (2004). Feature Extraction and Classification of the Auditory Brainstem Response Using Wavelet Analysis. In: López, J.A., Benfenati, E., Dubitzky, W. (eds) Knowledge Exploration in Life Science Informatics. KELSI 2004. Lecture Notes in Computer Science(), vol 3303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30478-4_15
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DOI: https://doi.org/10.1007/978-3-540-30478-4_15
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