Computer-Assisted Sleep Staging Based on Segmentation and Clustering

Abstract

In this paper, a method is presented that can be used to automatically classify sleep states in an all-night polysomnogram (PSG) to generate a hypnogram for the assessment of sleep-related disorders. The method is based on ideas of segmentation and classification (clustering) using sleep related features. Segments are clustered to generate groups of similar patterns that can subsequently be labeled as one of the accepted clinically relevant sleep stages. Each PSG is processed independently to generate classes to similar patterns in an unsupervised manner, thus achieving pseudo-natural classes that are independent of any classification criterion.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412441

Entities

People

  • Jean Gotman
  • Rajeev Agarwal

Organizations

  • Concordia University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Amplitude
  • Artifacts
  • Boundaries
  • Classification
  • Clustering
  • Computer Vision
  • Computers
  • Data Sets
  • Detection
  • Electroencephalography
  • Engineering
  • Eye Movements
  • Feature Extraction
  • Mathematical Models
  • Self Organizing Systems

Fields of Study

  • Computer science

Readers

  • Circadian Sleep-Wake Regulation and Chronobiology
  • Computer Vision.
  • Neural Network Machine Learning.