Automatic detection of single slow eye movements and analysis of their changes at sleep onset

Filippo Cona, Fabio Pizza, Federica Provini, Elisa Magosso

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An algorithm that can automatically identify slow eye movements from the electro-oculogram is presented. The automatic procedure is trained using the visual classification of an expert scorer. The algorithm makes use of both the spectral and morphological signal information to detect single slow eye movements. On the basis of this detection some parameters that characterize the slow eye movements (amplitude, duration, velocity and number) are extracted. A few possible applications of the algorithm are shown by means of a preliminary study: the average patterns of slow eye movements parameters at sleep onset are evaluated for healthy volunteers and for patients affected by obstructive sleep apnea syndrome. Finally, general considerations are drawn regarding the clinical interest of the study.

Original languageEnglish
Title of host publicationIJCCI 2013 - Proceedings of the 5th International Joint Conference on Computational Intelligence
Pages474-481
Number of pages8
Publication statusPublished - 2013
Event5th International Joint Conference on Computational Intelligence, IJCCI 2013 - Vilamoura, Algarve, Portugal
Duration: Sep 20 2013Sep 22 2013

Other

Other5th International Joint Conference on Computational Intelligence, IJCCI 2013
CountryPortugal
CityVilamoura, Algarve
Period9/20/139/22/13

Keywords

  • Automatic Detection
  • Sleep Onset
  • Slow Eye Movements
  • Template Matching

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'Automatic detection of single slow eye movements and analysis of their changes at sleep onset'. Together they form a unique fingerprint.

  • Cite this

    Cona, F., Pizza, F., Provini, F., & Magosso, E. (2013). Automatic detection of single slow eye movements and analysis of their changes at sleep onset. In IJCCI 2013 - Proceedings of the 5th International Joint Conference on Computational Intelligence (pp. 474-481)