Classification of Sleep Stages in Infants: A Neuro Fuzzy Approach

Abstract

An ANFIS based neuro-fuzzy system to classify sleep-waking states and stages in healthy infants has been developed. The classifier takes five input patterns identified from polysomnographic recordings on 20 s frames and assigns them to one out of five possible classes (WA, NREM-I, NREM-II, NREM-III and IV or REM). Eight polysomnographic recordings of healthy infants were studied, making a total of 3510 frames. Of these, four recordings were used for training, two for validation and two for testing. Results on the testing data achieved on average 88.2% of expert agreement in sleep- waking state-stage classification. These results were compared with the ones obtained using a multi-layer perceptron neural network (87.3%) and by applying the expert's rules for sleep classification (86.7%). The neuro-fuzzy approach also rendered fuzzy classification rules, which were analyzed and compared with the expert's rules.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410091

Entities

People

  • C. A. Holzmann
  • C. A. Perez
  • C. M. Held
  • J. E. Heiss
  • P. A. Estevez

Organizations

  • University of Chile

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Agreements
  • Algorithms
  • Classification
  • Data Acquisition
  • Data Sets
  • Detection
  • Electrical Engineering
  • Engineering
  • Frequency
  • Identification
  • Identification Systems
  • Learning
  • Machine Learning
  • Neural Networks
  • Supervised Machine Learning
  • Test Sets

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks