Combination of AI Components for Biosignal Processing- Application to Sleep Stage Recognition

Abstract

We present a novel approach to combining artificial intelligence components for biomedical signal processing. The modular algorithm mimics the step-by-step type procedure of a human expert and includes the two assessment steps most important for sleep stage scoring, pattern recognition in electrophysiological signal channels and rule evaluation for classifying the current sequence of patterns. The application of sleep stage scoring is a complex task in medical informatics. The ARTISANA (artificial intelligence in sleep analysis) algorithm we have developed provides high rates of correspondence with the results produced by human experts. Additional features are the transparent decision-making process and information about the detailed structure of sleep. This has been achieved by utilizing neural networks for pattern recognition and neuro-fuzzy systems for rule evaluation. The AI components chosen to perform these two classification steps were particularly successful due to their individual strengths.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412050

Entities

People

  • A. Bolz
  • J. Schoechlin
  • M. H. Schwaibold
  • T. Penzel

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Classification
  • Dimensionality Reduction
  • Engineering
  • Information Systems
  • Military Research
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Signal Processing
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Circadian Sleep-Wake Regulation and Chronobiology
  • Computer Vision.
  • Geospatial Intelligence and Artificial Intelligence Analytics

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Biotechnology