TY - GEN
T1 - Classification of the auditory brainstem response (ABR) using wavelet analysis and Bayesian network
AU - Zhang, Rui
AU - McAllister, Gerry
AU - Scotney, Bryan
AU - McClean, Sally
AU - Houston, Glen
PY - 2005
Y1 - 2005
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 it typically requires up to 2000 repetitions. This number of repetitions can be very difficult, time consuming and uncomfortable for some subjects. In this study a method combining the wavelet analysis and the Bayesian network is introduced to reduce the required number of repetitions, which could offer a great advantage in the clinical situation. The important features of the ABR are extracted by thresholding and matching the wavelet coefficients. These extracted features are then used as the variables to build up the Bayesian network for classifying the ABR. 172 ABRs with 64 repetitions are applied in this study to learn the Bayesian network and estimate the conditional probability tables (CPTs). A further 142 ABRs with 64 repetitions are used to test the network. Moreover, this Bayesian network can also be applied to classify the ABRs with 128 repetitions.
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 it typically requires up to 2000 repetitions. This number of repetitions can be very difficult, time consuming and uncomfortable for some subjects. In this study a method combining the wavelet analysis and the Bayesian network is introduced to reduce the required number of repetitions, which could offer a great advantage in the clinical situation. The important features of the ABR are extracted by thresholding and matching the wavelet coefficients. These extracted features are then used as the variables to build up the Bayesian network for classifying the ABR. 172 ABRs with 64 repetitions are applied in this study to learn the Bayesian network and estimate the conditional probability tables (CPTs). A further 142 ABRs with 64 repetitions are used to test the network. Moreover, this Bayesian network can also be applied to classify the ABRs with 128 repetitions.
UR - http://www.scopus.com/inward/record.url?scp=27544493738&partnerID=8YFLogxK
M3 - Conference Proceeding
AN - SCOPUS:27544493738
SN - 1063-7125
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 485
EP - 490
BT - Proceedings - 18th IEEE Symposium on Computer-Based Medical Systems
T2 - 18th IEEE Symposium on Computer-Based Medical Systems
Y2 - 23 June 2005 through 24 June 2005
ER -