TY - GEN
T1 - Coupling wavelet transform with Bayesian network to classify auditory brainstem responses
AU - Zhang, R.
AU - McAllister, G.
AU - Scotney, B.
AU - McClean, S.
AU - Houston, G.
PY - 2005
Y1 - 2005
N2 - In this work, a method that combines wavelet transform and Bayesian network is developed for the classification of the auditory brainstem response (ABR). First the wavelet transform is applied to extract the important features of the ABR by thresholding and matching the wavelet coefficients. A Bayesian network is then built up based on several variables obtained from these significant wavelet coefficients. In order to evaluate the performance of this approach, stratified 10-fold cross-validation is used and the network is evaluated on subject-dependent test sets (drawn from the same subjects from which the training set was derived). In particular, the data analyzed here are the ABR data with only fewer repetitions (64 or 128 repetitions) and this offers the great advantage of reducing the total time of recording, which is very beneficial to both the clinicians and the patients. Finally, a preprocessing method based on Woody averaging is applied to adjust the latency shift of the ABR data and it enhances the results.
AB - In this work, a method that combines wavelet transform and Bayesian network is developed for the classification of the auditory brainstem response (ABR). First the wavelet transform is applied to extract the important features of the ABR by thresholding and matching the wavelet coefficients. A Bayesian network is then built up based on several variables obtained from these significant wavelet coefficients. In order to evaluate the performance of this approach, stratified 10-fold cross-validation is used and the network is evaluated on subject-dependent test sets (drawn from the same subjects from which the training set was derived). In particular, the data analyzed here are the ABR data with only fewer repetitions (64 or 128 repetitions) and this offers the great advantage of reducing the total time of recording, which is very beneficial to both the clinicians and the patients. Finally, a preprocessing method based on Woody averaging is applied to adjust the latency shift of the ABR data and it enhances the results.
KW - Auditory brainstem response
KW - Bayesian network
KW - Classification
KW - Stratified 10-fold cross-validation
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=33846901186&partnerID=8YFLogxK
U2 - 10.1109/iembs.2005.1616263
DO - 10.1109/iembs.2005.1616263
M3 - Conference Proceeding
AN - SCOPUS:33846901186
SN - 0780387406
SN - 9780780387409
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 7568
EP - 7571
BT - Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Y2 - 1 September 2005 through 4 September 2005
ER -