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

Mihai Popescu, Stergios Papadimitriou, Dimitrios Karamitsos, Anastasios Bezerianos

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)38-50
Number of pages13
JournalAudiology and Neuro-Otology
Volume4
Issue number1
DOIs
StatePublished - Jan 1 1999

Keywords

  • Brainstem auditory evoked potentials
  • Neural networks
  • Wavelet transform

ASJC Scopus subject areas

  • Physiology
  • Otorhinolaryngology
  • Sensory Systems
  • Speech and Hearing

Fingerprint Dive into the research topics of 'Adaptive denoising and multiscale detection of the V wave in brainstem auditory evoked potentials'. Together they form a unique fingerprint.

  • Cite this