Combining Wavelet Analysis and Bayesian Networks for the Classification of Auditory Brainstem Response

R Zhang, G McAllister, BW Scotney, SI McClean, G Houston

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


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, typically requiring 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 wavelet analysis and Bayesian networks is introduced to reduce the required number of repetitions, which could offer a great advantage in the clinical situation. 314 ABRs with 64 repetitions and 155 ABRs with 128 repetitions recorded from eight subjects are used here. A wavelet transform is applied to each of the ABRs, and the important features of the ABRs are extracted by thresholding and matching the wavelet coefficients. The significant wavelet coefficients that represent the extracted features of the ABRs are then used as the variables to build the Bayesian network for classification of the ABRs. In order to estimate the performance of this approach, stratified ten-fold cross-validation is used.
Original languageEnglish
Pages (from-to)458-467
JournalIEEE Transactions on Information Technology in BioMedicine
Issue number3
Publication statusPublished (in print/issue) - 1 Jul 2006

Bibliographical note

Other Details
This paper was selected (with re-review) for journal publication from papers presented at the 18th IEEE International Symposium on Computer-Based Medical Systems, June 2005. The paper develops an algorithm that enables hearing response to be assessed using less than one-tenth of the data (from auditory brainstem responses to stimuli) typically required by techniques currently used by audiologists. Thus the length of assessment sessions may be reduced, with corresponding reductions in patient discomfort and audiologists' time. Hearing classification accuracy has been evaluated in collaboration with (and using data provided by) an Audiological Scientist at the Royal Hospitals, Belfast (Glen Houston).


  • Auditory brainstem response (ABR)
  • Bayesian networks
  • classification
  • stratified tenfold cross validation
  • wavelet analysis


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