Computational Waveform Analysis and Classification of Auditory Brainstem Evoked Potentials

Abstract

The widely used quantitative descriptors of amplitude and latency of evoked potentials, for peaks and troughs along the waveform, relate to only a limited number of points along the waveform, ignoring the interposed data. Moreover, these descriptors are typically determined manually, rendering them susceptible to user bias. We propose and demonstrate a machine-scoring algorithm for the identification and measurement of Auditory Brainstem Evoked Potentials (ABEP) peaks I, III and V. We further introduce an algorithm for the quantitative analysis of ABEP by waveform, and for clustering records according to waveform characteristics. The results of computerized peak identification and measurement, without user intervention, were correlated with manual measurements of the same peaks in a large number of waveforms. The waveform analysis and classification procedure differentiated waveforms to monaural left, monaural right and binaural stimulation, as well as according to the recording montage. These results underscore the advantages of using information in the waveform of ABEP, which has so far been overlooked. The automated algorithms for evaluation of ABEP by waveform hold the promise of a more comprehensive and consistent evaluation, and hence improved sensitivity.

Key Words:

• evoked potentials
• waveform analysis
• clustering