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Automatic sleep stage identification: difficulties and possible solutions

Nadezda Sukhorukova
  • Function : Author
Andrew Stranieri
  • Function : Author
Bahadorreza Ofoghi
  • Function : Author
Peter Vamplew
  • Function : Author
M. Saleem
  • Function : Author
Liping Ma
  • Function : Author
Julien Ugon
  • Function : Author
Nial Muecke
  • Function : Author
Hélène Amiel
  • Function : Author
Carole Philippe
  • Function : Author
Ahmed Bani-Mustafa
  • Function : Author
Shamsul Huda
  • Function : Author
Marcello Bertoli
  • Function : Author
Pierre Lévy
  • Function : Author

Abstract

The diagnosis of many sleep disorders is a labor intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determining a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a single approach.
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Dates and versions

hal-01288724 , version 1 (15-03-2016)

Identifiers

  • HAL Id : hal-01288724 , version 1

Cite

Nadezda Sukhorukova, Andrew Stranieri, Bahadorreza Ofoghi, Peter Vamplew, M. Saleem, et al.. Automatic sleep stage identification: difficulties and possible solutions. Fourth Australasian Workshop on Health Informatics and Knowledge Management, HIKM'2010, Jan 2010, Brisbane, Australia. pp.39-44. ⟨hal-01288724⟩
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