Fast detection of wave V in ABRs using a smart single sweep analysis system

Abstract

The analysis of auditory brainstem responses (ABRs) is accepted to be the most reliable method for the objective diagnosis and quantification of hearing loss in new-borns. However, in currently available setups, a large number of sweeps has to be averaged to obtain a meaningful signal at low stimulation levels due to a poor signal-to-noise ratio. In this study, we present a new approach to the detection of wave V in ABRs using a smart single sweep analysis system. A small number of sweeps is decomposed by optimized tight frames and evaluated by a kernel based novelty detection machine. This hybrid supervised learning scheme is combined with an inter-sweep dissimilarity tracing for the final decision making. At the challenging stimulation level of 30 dB, our system reached a reasonable specificity and sensitivity for the detection of wave V in a fraction of the measurement time of conventional schemes.