Adaptive denoising and multiscale detection of the V wave in brainstem auditory evoked potentials

This paper describes a wavelet-transform-based system for the V wave identification in brainstem auditory evoked potentials (BAEP). The system combines signal denoising and rule-based localization modules. The signal denoising module has the potential of effective noise reduction after signal averaging. It analyses adaptively the evolution of the wavelet transform maxima across scales. The singularities of the signal create wavelet maxima with different properties from those of the induced noise. A non-linear filtering process implemented with a neural network extracts out the noise-induced maxima. The filtered wavelet details are subsequently analysed by the rule-based localization module for the automatic identification of the V wave. In the first phase, it implements a set of statistical observations as well as heuristic criteria used by human experts in order to classify the IV-V complex. At the second phase, using a multiscale focusing algorithm, the IV and V waves are positioned on the BAEP signal. Our experiments revealed that the system provides accurate results even for signals exhibiting unclear IV-V complexes.