TY - GEN
T1 - Feature extraction and classification of the auditory brainstem response using wavelet analysis
AU - Zhang, Rui
AU - McAllister, Geny
AU - Scotney, Bryan
AU - McClean, Sally
AU - Houston, Glen
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=22944488279&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-30478-4_15
DO - 10.1007/978-3-540-30478-4_15
M3 - Conference Proceeding
AN - SCOPUS:22944488279
VL - 3303
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 169
EP - 180
BT - International Symposium KELSI 2004: Knowledge Exploration in Life Science Informatics
T2 - International Symposium KELSI 2004: Knowledge Exploration in Life Science Informatics
Y2 - 25 November 2004 through 26 November 2004
ER -