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Similarity Grouping of Human Sleep Recordings Using EEG and ECG

  • Amro Khasawneh
  • Sergio A. Alvarez
  • Carolina Ruiz
  • Shivin Misra
  • Majaz Moonis
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 273)

Abstract

Characterizing variations in sleep stage composition is important in the scientific study of sleep. We use clustering, a form of unsupervised machine learning, to seek naturally occurring types within a collection of records that describe the sleep stage composition of 244 all-night human sleep studies. The results uncover a hierarchy of sleep composition types differentiated primarily by sleep efficiency or total sleep time and by the relative proportion of slow-wave sleep. The potential significance of these sleep type clusters for sleep medicine is suggested by associations between sleep type and health-related variables such as body-mass index, smoking frequency, and heart disease. EEG and ECG features, including spectral power distribution and measures of heart-rate variability, differ significantly among sleep types. The EEG signal provides sufficient information for an approximate reconstruction of the sleep type clusters, while ECG alone is found to be insufficient.

Keywords

Sleep architecture Hypnogram Clustering Machine learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Amro Khasawneh
    • 1
  • Sergio A. Alvarez
    • 2
  • Carolina Ruiz
    • 1
  • Shivin Misra
    • 1
  • Majaz Moonis
    • 3
  1. 1.Department of Computer ScienceWorcester Polytechnic InstituteWorcesterU.S.A.
  2. 2.Department of Computer ScienceBoston CollegeChestnut HillU.S.A.
  3. 3.Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterU.S.A.

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