title: Fuzzy sets for feature identification in biomedical signals with self-assessment of reliability: an adaptable algorithm modeling human procedure in BAEP analysis.
abstract: The analysis of biomedical signals has mostly been restricted to traditional signal-processing methods. This article proposes a different approach, applied to the evaluation of brainstem auditory evoked potentials. An automatic peak identification method is described which uses criteria applied by human evaluators in visual analysis. These criteria are defined as fuzzy sets and are combined using fuzzy operations, thus reflecting the weighting of different facts by humans. Membership values are interpreted as degrees of satisfaction which indicate the degree to which a sample satisfies a given criterion. The system judges its own performance in terms of degrees of reliability. Tests on a large set of clinical data showed high performance on good and average-quality curves. A substantial drawback was the assignment of too many peaks in poor potentials. The approaches presented here can easily be applied to similar one-dimensional (and higher) signal analysis tasks.
authors.affiliation: Computer Science Department, University of Magdeburg, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Piater
authors.forename: F
authors.initials: F
authors.lastname: Stuchlik
authors.forename: H
authors.initials: H
authors.lastname: von Specht
authors.forename: R
authors.initials: R
authors.lastname: Mühler
mesh_terms: D000465:Algorithms; D016057:Evoked Potentials, Auditory, Brain Stem; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual; D011930:Reaction Time; D015203:Reproducibility of Results; D012647:Self-Assessment; D012815:Signal Processing, Computer-Assisted