Classification of brain-stem auditory evoked potentials by syntactic methods

A syntactic pattern recognition procedure for classification of brain-stem auditory evoked potential (BSAEP) is presented. A pre-processing stage of zero-phase bandpass filtering enhances the peaks and suppresses the noise. A finite-state grammar was designed to identify the peaks. Attributes of the peaks (latencies and amplitudes) that are identified are checked for their acceptability. A training run on 70 subjects of known diagnosis was performed to fine-tune the system and build up necessary acceptance criteria. Peak latency differences are used for the classification rather than absolute peak latencies. Acceptance criteria for peak latency differences were empirically optimized. A data base of normal BSAEPs, created during the training run, was updated and used during the test run. Test of the classifier using 60 subjects yielded a classification accuracy of 83%. The classifier has acceptable accuracy and can be modified for other evoked potentials such as visual and somatosensory by establishing relevant attribute tables.